This paper investigates various topologies on the space of equivalent channels, establishing their properties, relationships, and limitations, including the natural, strong, noisiness, and weak-* topologies, with implications for metrizability and compactness.
Contribution
It characterizes the properties of all natural topologies on the space of equivalent channels and introduces the noisiness topology, linking it to the weak-* topology and analyzing their structural features.
Findings
01
Every natural topology is $\sigma$-compact, separable, and path-connected.
02
No natural topology is completely metrizable if the input alphabet size is at least 2.
03
The noisiness topology is shown to be natural and coincides with the weak-* topology.
Abstract
Two channels are said to be equivalent if they are degraded from each other. The space of equivalent channels with input alphabet X and output alphabet Y can be naturally endowed with the quotient of the Euclidean topology by the equivalence relation. A topology on the space of equivalent channels with fixed input alphabet X and arbitrary but finite output alphabet is said to be natural if and only if it induces the quotient topology on the subspaces of equivalent channels sharing the same output alphabet. We show that every natural topology is σ-compact, separable and path-connected. On the other hand, if ∣X∣≥2, a Hausdorff natural topology is not Baire and it is not locally compact anywhere. This implies that no natural topology can be completely metrized if ∣X∣≥2. The finest natural topology, which we call the strong topology, is shown to be compactly…
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Two channels are said to be equivalent if they are degraded from each other. The space of equivalent channels with input alphabet X and output alphabet Y can be naturally endowed with the quotient of the Euclidean topology by the equivalence relation. We show that this topology is compact, path-connected and metrizable. A topology on the space of equivalent channels with fixed input alphabet X and arbitrary but finite output alphabet is said to be natural if and only if it induces the quotient topology on the subspaces of equivalent channels sharing the same output alphabet. We show that every natural topology is σ-compact, separable and path-connected. On the other hand, if ∣X∣≥2, a Hausdorff natural topology is not Baire and it is not locally compact anywhere. This implies that no natural topology can be completely metrized if ∣X∣≥2. The finest natural topology, which we call the strong topology, is shown to be compactly generated, sequential and T4. On the other hand, the strong topology is not first-countable anywhere, hence it is not metrizable. We show that in the strong topology, a subspace is compact if and only if it is rank-bounded and strongly-closed. We provide a necessary and sufficient condition for a sequence of channels to converge in the strong topology. We introduce a metric distance on the space of equivalent channels which compares the noise levels between channels. The induced metric topology, which we call the noisiness topology, is shown to be natural. We also study topologies that are inherited from the space of meta-probability measures by identifying channels with their Blackwell measures. We show that the weak-∗ topology is exactly the same as the noisiness topology and hence it is natural. We prove that if ∣X∣≥2, the total variation topology is not natural nor Baire, hence it is not completely metrizable. Moreover, it is not locally compact anywhere. Finally, we show that the Borel σ-algebra is the same for all Hausdorff natural topologies.
I Introduction
A topology on a given set is a mathematical structure that allows us to formally talk about the neighborhood of a given point of the set. This makes it possible to define continuous mappings and converging sequences. Topological spaces generalize metric spaces which are mathematical structures that specify distances between the points of the space. Links between information theory and topology were investigated in [1]. In this paper, we aim to construct meaningful topologies and metrics for the space of equivalent channels sharing a common input alphabet.
Let X and Y be two fixed finite sets. Every discrete memoryless channel (DMC) with input alphabet X and output alphabet Y can be determined by its transition probabilities. Since there are ∣X∣×∣Y∣ such probabilities, the space of all channels from X to Y can be seen as a subset of R∣X∣×∣Y∣. Therefore, this space can be naturally endowed with the Euclidean metric, or any other equivalent metric. A generalization of this topology to infinite input and output alphabets was considered in [2].
There are a few drawbacks for this approach. For example, consider the case where X=Y=F2:={0,1}. The binary symmetric channels BSC(ϵ) and BSC(1−ϵ) have non-zero Euclidean distance if ϵ=21. On the other hand, BSC(ϵ) and BSC(1−ϵ) are completely equivalent from an operational point of view: both channels have exactly the same probability of error under optimal decoding for any fixed code. Moreover, any sub-optimal decoder for one channel can be transformed to a sub-optimal decoder for the other channel without changing the probability of error nor the computational complexity. This is why it makes sense, from an information-theoretic point of view, to identify equivalent channels and consider them as one point in the space of “equivalent channels”.
The limitation of the Euclidean metric is clearer when we consider channels with different output alphabets. For example, BSC(21) and BEC(1) are completely equivalent but they do not have the same output alphabet, and so there is no way to compare them with the Euclidean metric because they do not belong to the same space.
The standard approach to solve this problem is to find a “canonical sufficient statistic” and find a representation of each channel in terms of this sufficient statistic. This makes it possible to compare channels with different output-alphabets. One standard sufficient statistic that has been widely used for binary-input channels is the log-likelihood ratio. Each binary-input channel can be represented as a density of log-likelihood ratios (called L-density in [3]). This representation makes it possible to “topologize” the space of “equivalent” binary-input channels by considering the topology of convergence in distribution [3]. A similar approach can be adopted for non-binary-input channels (see [4] and [5]). Another (equivalent) way to “topologize” the space of equivalent channels is by using the Le Cam deficiency distance [6].
The current formulation of this topology cannot be generalized to the quantum setting because it is given in terms of posterior probabilities which do not have quantum analogues. Therefore, if we want to generalize this topology to the space of equivalent quantum channels and equivalent classical-quantum channels, it is crucial to find a formulation for this topology that does not explicitly depend on posterior probabilities.
Another issue (which is secondary and only relevant for conceptual purposes) is that the current formulation of this topology does not allow us to see it as a “natural topology”. Consider a fixed output alphabet Y and let us focus on the space of “equivalent channels” from X to Y. Since this space is the quotient of the space of channels from X to Y, which is naturally topologized by the Euclidean metric, it seems that the most natural topology on this space is the quotient of the Euclidean topology by the equivalence relation. This motivates us to consider a topology on the space of “equivalent channels” with input alphabet X and arbitrary but finite output alphabet as natural if and only if it induces the quotient topology on the subspaces of “equivalent channels” from X to Y for any output alphabet Y. A legitimate question to ask now is whether the L-density topology is natural in this sense or not.
In this paper, we study general and particular natural topologies on DMC spaces. In Section II, we provide a brief summary of the basic concepts and theorems in general topology. The measure-theoretic notations that we use are introduced in section III. The space of channels from X to Y and its topology is studied in Section IV. We formally define the equivalence relation between channels in section V. It is shown that the equivalence class of a channel can be determined by the distribution of its posterior probability distribution. This is the standard generalization of L-densities to non-binary-input channels. This distribution is called the Blackwell measure of the channel. In section VI, we study the space of equivalent channels from X to Y and the quotient topology.
In Section VII, we define the space of equivalent channels with input alphabet X and we study the properties of general natural topologies. The finest natural topology, which we call the strong topology is studied in Section VIII. A metric for the space of equivalent channels is proposed in section IX. The induced topology by this metric is called the noisiness topology. In section X, we study the topologies that are inherited from the space of meta-probability measures by identifying equivalent channels with their Blackwell measures. We show that the weak-∗ topology (which is the standard generalization of the L-density topology to non-binary-input channels) is exactly the same as the noisiness topology. The total variation topology is also investigated in section X. The Borel σ-algebra of Hausdorff natural topologies is studied in section XI.
The continuity (under the topologies introduced here) of mappings that are relevant to information theory (such as capacity, mutual information, Bhattacharyya parameter, probability of error of a fixed code, optimal probability of error of a given rate and blocklength, channel sums and products, etc …) is studied in [7]. We also study the polarization process of Arıkan [8] and its convergence under various topologies in [9].
II Preliminaries
In this section, we recall basic definitions and well known theorems in general topology. The reader who is already familiar with the basic concepts of topology may skip this section and refer to it later if necessary. Proofs of all non-referenced facts can be found in any standard textbook on general topology (e.g., [10]). Definitions and theorems that may not be widely known can be found in Sections II-J, II-N and II-O.
II-A Set-theoretic notations
For every integer n>0, we denote the set {1,…,n} as [n].
The set of mappings from a set A to a set B is denoted as BA.
Let A be a subset of B. The indicator mapping\mathds1A,B:B→{0,1} of A in B is defined as:
[TABLE]
If the superset B is clear from the context, we simply write \mathds1A to denote the indicator mapping of A in B.
The power set of B is the set of subsets of B. Since every subset of B can be identified with its indicator mapping, we denote the power set of B as 2B:={0,1}B.
A collection A⊂2B of subsets of B is said to be finer than another collection A′⊂2B if A′⊂A. If this is the case, we also say that A′ is coarser than A.
Let (Ai)i∈I be a collection of arbitrary sets indexed by I. The disjoint union of (Ai)i∈I is defined as i∈I∐Ai=i∈I⋃(Ai×{i}). For every i∈I, the ith-canonical injection is the mapping ϕi:Ai→j∈I∐Aj defined as ϕi(xi)=(xi,i). If no confusions can arise, we can identify Ai with Ai×{i} through the canonical injection. Therefore, we can see Ai as a subset of j∈I∐Aj for every i∈I.
A relationR on a set T is a subset of T×T. For every x,y∈T, we write xRy to denote (x,y)∈R.
A relation is said to be reflexive if xRx for every x∈T. It is symmetric if xRy implies yRx for every x,y∈T. It is anti-symmetric if xRy and yRx imply x=y for every x,y∈T. It is transitive if xRy and yRz imply xRz for every x,y,z∈T.
An order relation is a relation that is reflexive, anti-symmetric and transitive. An equivalence relation is a relation that is reflexive, symmetric and transitive.
Let R be an equivalence relation on T. For every x∈T, the set x^={y∈T:xRy} is the R-equivalence class of x. The collection of R-equivalence classes, which we denote as T/R, forms a partition of T, and it is called the quotient space of T by R. The mapping ProjR:T→T/R defined as ProjR(x)=x^ for every x∈T is the projection mapping onto T/R.
II-B Topological spaces
A topological space is a pair (T,U), where U⊂2T is a collection of subsets of T satisfying:
•
\o∈U and T∈U.
•
The intersection of a finite collection of members of U is also a member of U.
•
The union of an arbitrary collection of members of U is also a member of U.
If (T,U) is a topological space, we say that U is a topology on T.
The power set 2T of T is clearly a topology. It is called the discrete topology on T.
If A is a an arbitrary collection of subsets of T, we can construct a topology on T starting from A as follows:
[TABLE]
This is the coarsest topology on T that contains A. It is called the topology on T generated by A.
Let (T,U) be a topological space. The subsets of T that are members of U are called the open sets of T. Complements of open sets are called closed sets. We can easily see that the closed sets satisfy the following:
•
\o and T are closed.
•
The union of a finite collection of closed sets is closed.
•
The intersection of an arbitrary collection of closed sets is closed.
Let A be an arbitrary subset of T. The closurecl(A) of A is the smallest closed set containing A:
[TABLE]
The interiorA∘ of A is the largest open subset of A:
[TABLE]
If A⊂T and cl(A)=T, we say that A is dense in T.
(T,U) is said to be separable if there exists a countable subset of T that is dense in T.
A subset O of T is said to be a neighborhood of x∈T if there exists an open set U∈U such that x∈U⊂O.
A neighborhood basis of x∈T is a collection O of neighborhoods of x such that for every neighborhood O of x, there exists O′∈O such that O′⊂O.
We say that (T,U) is first-countable if every point x∈T has a countable neighborhood basis.
A collection of open sets B⊂U is said to be a base for the topology U if every open set U∈U can be written as the union of elements of B.
We say that (T,U) is a second-countable space if the topology U has a countable base.
It is a well known fact that every second-countable space is first-countable and separable.
We say that a sequence (xn)n≥0 of elements of Tconverges to x∈T if for every neighborhood O of x, there exists n0≥0 such that for every n≥n0, we have xn∈O. We say that x is a limit of the sequence (xn)n≥0. Note that the limit does not need to be unique if there is no constraint on the topology.
II-C Separation axioms
(T,U) is said to be a T1-space if for every x,y∈T, there exists an open set U∈U such that x∈U and y∈/U. It is easy to see that (T,U) is T1 if and only if all singletons are closed.
(T,U) is said to be a Hausdorff space (or T2-space) if for every x,y∈T, there exist two open sets U,V∈U such that x∈U, y∈V and U∩V=\o.
If (T,U) is Hausdorff, the limit of every converging sequence is unique.
(T,U) is said to be regular if for every x∈T and every closed set F not containing x, there exist two open sets U,V∈U such that x∈U, F⊂V and U∩V=\o.
(T,U) is said to be normal if for every two disjoint closed sets A and B, there exist two open sets U,V∈U such that A⊂U, B⊂V and U∩V=\o.
If (T,U) is normal, disjoint closed sets can be separated by disjoint closed neighborhoods. I.e., for every two disjoint closed sets A and B, there exist two open sets U,U′∈U and two closed sets K,K′ such that A⊂U⊂K, B⊂U′⊂K′ and K∩K′=\o.
(T,U) is said to be a T3-space if it is both T1 and regular.
(T,U) is said to be a T4-space if it is both T1 and normal.
It is easy to see that T4⇒T3⇒T2⇒T1.
II-D Relativization
If (T,U) is a topological space and A is an arbitrary subset of T, then A inherits a topology UA from (T,U) as follows:
[TABLE]
It is easy to check that UA is a topology on A.
If (T,U) is first-countable (respectively second-countable, or Hausdorff), then (A,UA) is first-countable (respectively second-countable, or Hausdorff).
If (T,U) is normal and A is closed, then (A,UA) is normal.
The union of a countable number of separable subspaces is separable.
II-E Continuous mappings
Let (T,U) and (S,V) be two topological spaces. A mapping f:T→S is said to be continuous if for every V∈V, we have f−1(V)∈U.
f:T→S is an open mapping if f(U)∈V whenever U∈U.
f:T→S is a closed mapping if f(F) is closed in S whenever F is closed in T.
A bijection f:T→S is a homeomorphism if both f and f−1 are continuous. In this case, for every A⊂T, A∈U if and only if f(A)∈V. This means that (T,U) and (S,V) have the same topological structure and share the same topological properties.
II-F Compact spaces and sequentially compact spaces
(T,U) is a compact space if every open cover of T admits a finite sub-cover. I.e., if (Ui)i∈I is a collection of open sets such that T=i∈I⋃Ui then there exists n>0 and i1,…,in∈I such that T=j=1⋃nUij.
If (T,U) is compact, then every closed subset of T is compact (with respect to the inherited topology).
If f:T→S is a continuous mapping from a compact space (T,U) to an arbitrary topological space (S,V), then f(T) is compact.
If A is a compact subset of a Hausdorff topological space, then A is closed.
(T,U) is said to be locally compact if every point has at least one compact neighborhood. A compact space is automatically locally compact.
If (T,U) is Hausdorff and locally compact, then for every point x∈T and every neighborhood O of x, O contains a compact neighborhood of x.
A compact Hausdorff space is always normal.
(T,U) is a σ-compact space if it is the union of a countable collection of compact subspaces.
(T,U) is countably compact if every countable open cover of T admits a finite sub-cover. This is a weaker condition compared to compactness.
(T,U) is said to be sequentially compact if every sequence in T has a converging subsequence. In general, compactness does not imply sequential compactness nor the other way around.
II-G Connected spaces
(T,U) is a connected space if it satisfies one of the following equivalent conditions:
•
T cannot be written as the union of two disjoint non-empty open sets.
•
T cannot be written as the union of two disjoint non-empty closed sets.
•
The only subsets of T that are both open and closed are \o and T.
•
Every continuous mapping from T to {0,1} is constant, where {0,1} is endowed with the discrete topology.
(T,U) is path-connected if every two points of T can be joined by a continuous path. I.e., for every x,y∈T, there exists a continuous mapping f:[0,1]→T such that f(0)=x and f(1)=y, where [0,1] is endowed with the well known Euclidean topology111See Section II-K for the definition of the Euclidean metric and its induced topology.
A path-connected space is connected but the converse is not true in general.
A subset A of T is said to be connected (respectively path-connected) if (A,UA) is connected (respectively path-connected).
If (Ai)i∈I is a collection of connected (respectively path-connected) subsets of T such that i∈I⋂Ai=\o, then i∈I⋃Ai is connected (respectively path-connected).
II-H Product of topological spaces
Let {(Ti,Ui)}i∈I be a collection of topological spaces indexed by I. Let T=i∈I∏Ti be the product of this collection. For every j∈I, the jth-canonical projection is the mapping Projj:T→Tj defined as \operatorname*{Proj}_{j}\big{(}(x_{i})_{i\in I}\big{)}=x_{j}.
The product topologyU:=i∈I⨂Ui on T is the coarsest topology that makes all the canonical projections continuous. It can be shown that U is generated by the collection of sets of the form i∈I∏Ui, where Ui∈Ui for all i∈I, and Ui=Ti for only finitely many i∈I.
The product of T1 (respectively, Hausdorff, regular, T3, compact, connected, or path-connected) spaces is T1 (respectively, Hausdorff, regular, T3, compact, connected, or path-connected).
II-I Disjoint union
Let {(Ti,Ui)}i∈I be a collection of topological spaces indexed by I. Let T=i∈I∐Ti be the disjoint union of this collection. The disjoint union topologyU:=i∈I⨁Ui on T is the finest topology which makes all the canonical injections continuous. It can be shown that U∈U if and only if U∩Ti∈Ui for every i∈I.
A mapping f:T→S from (T,U) to a topological space (S,V) is continuous if and only if it is continuous on Ti for every i∈I.
The disjoint union of T1 (respectively Hausdorff) spaces is T1 (respectively Hausdorff). The disjoint union of two or more non-empty spaces is always disconnected.
Products are distributive with respect to the disjoint union, i.e., if (S,V) is a topological space then S×(i∈I∐Ti)=i∈I∐(S×Ti) and V⊗(i∈I⨁Ui)=i∈I⨁(V⊗Ui).
II-J Quotient topology
Let (T,U) be a topological space and let R be an equivalence relation on T. The quotient topology on T/R is the finest topology that makes the projection mapping ProjR continuous. It is given by
[TABLE]
Lemma 1**.**
Let f:T→S be a continuous mapping from (T,U) to (S,V). If f(x)=f(x′) for every x,x′∈T satisfying xRx′, then we can define a transcendent mappingf:T/R→S such that f(x^)=f(x′) for any x′∈x^. f is well defined on T/R . Moreover, f is a continuous mapping from (T/R,U/R) to (S,V).
If (T,U) is compact (respectively, connected, or path-connected), then (T/R,U/R) is compact (respectively, connected, or path-connected).
T/R is said to be upper semi-continuous if for every x^∈T/R and every open set U∈U satisfying x^⊂U, there exists an open set V∈U such that x^⊂V⊂U, and V can be written as the union of members of T/R.
The following Lemma characterizes upper semi-continuous quotient spaces:
Lemma 2**.**
[10]** T/R is upper semi-continuous if and only if ProjR is a closed mapping.
The following theorem is very useful to prove many topological properties for the quotient space:
Theorem 1**.**
[10]** Let (T,U) be a topological space, and let R be an equivalence relation on T such that T/R is upper semi-continuous and x^ is a compact subset of T for every x^∈T/R. If (T,U) is Hausdorff (respectively, regular, locally compact, or second-countable) then (T/R,U/R) is Hausdorff (respectively, regular, locally compact, or second-countable).
II-K Metric spaces
A metric space is a pair (M,d), where d:M×M→R+ satisfies:
•
d(x,y)=0 if and only if x=y for every x,y∈M.
•
Symmetry: d(x,y)=d(y,x) for every x,y∈M.
•
Triangle inequality: d(x,z)≤d(x,y)+d(y,z) for every x,y,z∈M.
If (M,d) is a metric space, we say that d is a metric (or distance) on M.
The Euclidean metric on Rn is defined as d(x,y)=i=1∑n(xi−yi)2, where x=(xi)1≤i≤n and y=(yi)1≤i≤n.
Rn is second countable. Moreover, a subset of Rn is compact if and only if it is bounded and closed.
For every x∈M and every ϵ>0, we define the open ball of center x and radius ϵ as:
[TABLE]
The metric topologyUd on Minduced by d is the coarsest topology on M which makes d a continuous mapping from M×M to R+. It is generated by all the open balls.
The metric topology is always T4 and first-countable. Moreover, (M,Ud) is separable if and only if it is second-countable.
Since every metric space is Hausdorff, we can see that every subset of a compact metric space is closed if and only if it is compact.
Every σ-compact metric space is second-countable.
For metric spaces, compactness and sequential compactness are equivalent.
A function f:M1→M2 from a metric space (M1,d1) to a metric space (M2,d2) is said to be uniformly continuous if for every ϵ>0, there exists δ>0 such that for every x,x′∈M1 satisfying d1(x,x′)<δ we have d2(f(x),f(x′))<ϵ.
If f:M1→M2 is a continuous mapping from a compact metric space (M1,d1) to an arbitrary metric space (M2,d2), then f is uniformly continuous.
A topological space (T,U) is said to be metrizable if there exists a metric d on T such that U is the metric topology on T induced by d.
The disjoint union of metrizable spaces is always metrizable.
The following theorem shows that all separable metrizable spaces are characterized topologically:
Theorem 2**.**
[10]** A topological space (T,U) is metrizable and separable if and only if it is Hausdorff, regular and second countable.
II-L Complete metric spaces
A sequence (xn)n≥0 is said to be a Cauchy sequence in (M,d) if for every ϵ>0, there exists n0≥0 such that for every n1,n2≥n0 we have d(xn1,xn2)<ϵ.
Every converging sequence is Cauchy, but the converse is not true in general.
A metric space is said to be complete if every Cauchy sequence converges in it.
A closed subset of a complete space is always complete.
A complete subspace of an arbitrary metric space is always closed.
Every compact metric space is complete, but the converse is not true in general.
For every metric space (M,d), there exists a superspace (M,d) containing M such that:
•
(M,d) is complete.
•
M is dense in (M,d).
•
d(x,y)=d(x,y) for every x,y∈M.
The space (M,d) is said to be a completion of (M,d).
II-M Polish spaces and Baire spaces
A topological space (T,U) that is both separable and completely metrizable (i.e., has a metrization that is complete) is called a Polish space.
A topological space is said to be a Baire space if the intersection of countably many dense open subsets is dense. The following facts can be found in [11]:
•
Every completely metrizable space is Baire.
•
Every compact Hausdorff space is Baire.
•
Every open subset of a Baire space is Baire.
II-N Sequential spaces
Sequential spaces were introduced by Franklin [12] to answer the following question: Assume we know all the converging sequences of a topological space. Is this enough to uniquely determine the topology of the space? Sequential spaces are the most general category of spaces for which converging sequences suffice to determine the topology.
Let (T,U) be a topological space. A subset U⊂T is said to be sequentially open if for every sequence (xn)n≥0 that converges to a point of U lies eventually in U, i.e., there exists n0≥0 such that xn∈U for every n≥n0. Clearly, every open subset of T is sequentially open, but the converse is not true in general.
A topological space (T,U) is said to be sequential if every sequentially open subset of T is open.
A mapping f:T→S from a sequential topological space (T,U) to an arbitrary topological space (S,V) is continuous if and only if for every sequence (xn)n≥0 in T that converges to x∈T, the sequence (f(xn))n≥0 converges to f(x) in (S,V) [12].
Every first-countable space is sequential. Therefore, every metrizable space is sequential.
•
The quotient of a sequential space is sequential.
•
All closed and open subsets of a sequential space are sequential.
•
Every countably compact sequential Hausdorff space is sequentially compact.
•
A topological space is sequential if and only if it is the quotient of a metric space.
II-O Compactly generated spaces
A topological space (T,U) is compactly generated if it is Hausdorff and for every subset F of T, F is closed if and only if F∩K is closed for every compact subset K of T. Equivalently, (T,U) is compactly generated if it is Hausdorff and for every subset U of T, U is open in T if and only if U∩K is open in K for every compact subset K of T.
All locally compact Hausdorff spaces are compactly generated.
•
All first-countable Hausdorff spaces are compactly generated. Therefore, every metrizable space is compactly generated.
•
A Hausdorff quotient of a compactly generated space is compactly generated.
•
If (T,U) is compactly generated and (S,V) is Hausdorff locally compact, then (T×S,U⊗V) is compactly generated.
III Measure-theoretic notations
In this section, we introduce the measure-theoretic notations that we are using. We assume that the reader is familiar with the basic definitions and theorems of measure theory.
III-A Probability measures
If A⊂2M is a collection of subsets of M, we denote the σ-algebra that is generated by A as σ(A).
The set of probability measures on (M,Σ) is denoted as P(M,Σ). If the σ-algebra Σ is known from the context, we simply write P(M) to denote the set of probability measures.
If P∈P(M,Σ) and {x} is a measurable singleton, we simply write P(x) to denote P({x}).
For every P1,P2∈P(M,Σ), the total variation distance between P1 and P2 is defined as:
[TABLE]
The space P(M,Σ) is a complete metric space under the total variation distance.
III-B Probabilities on finite sets
We always endow finite sets with their finest σ-algebra, i.e., the power set. In this case, every probability measure is completely determined by its value on singletons, i.e., if P is a measure on a finite set X, then for every A⊂X, we have
[TABLE]
If X is a finite set, we denote the set of probability distributions on X as ΔX. Note that ΔX is an (∣X∣−1)-dimensional simplex in RX. We always endow ΔX with the total variation distance and its induced topology. For every p1,p2∈ΔX, we have:
[TABLE]
Note that the total variation topology on ΔX is the same as the one inherited from the Euclidean topology of RX by relativisation. Since ΔX is a closed and bounded subset of RX, it is compact.
III-C Borel sets and the support of a measure
Let (T,U) be a Hausdorff topological space. The Borel σ-algebra of (T,U) is the σ-algebra generated by U. We denote the Borel σ-algebra of (T,U) as B(T,U). If the topology U is known from the context, we simply write B(T) to denote the Borel σ-algebra. The sets in B(T) are called the Borel sets of T.
The support of a probability measure P∈P(T,B(T)) is the set of all points x∈T for which every neighborhood has a strictly positive measure:
[TABLE]
If P is a probability measure on a Polish space, then P\big{(}T\setminus\operatorname*{supp}(P)\big{)}=0.
III-D Convergence of probability measures and the weak-∗ topology
We have many notions of convergence of probability measures. If the measurable space does not have a topological structure, we have two notions of convergence:
•
The total-variation convergence: we say that a sequence (Pn)n≥0 of probability measures in P(M,Σ) converges in total variation to P∈P(M,Σ) if and only if n→∞lim∥Pn−P∥TV=0.
•
The strong convergence: we say that a sequence (Pn)n≥0 in P(M,Σ) strongly converges to P∈P(M,Σ) if and only if n→∞limPn(A)=P(A) for every A∈Σ.
Clearly, total-variation convergence implies strong convergence. The converse is not true in general. However, if we are working in the Borel σ-algebra of a Polish space T and (Pn)n≥0 strongly converges to a finitely supported probability measure P, then
[TABLE]
which implies that (Pn)n≥0 also converges to P in total variation. Therefore, in a Polish space, total variation convergence and strong convergence to finitely supported probability measures are equivalent.
Let (T,U) be a Hausdorff topological space. We say that a sequence (Pn)n≥0 of probability measures in P(T,B(T)) weakly-∗ converges to P∈P(T,B(T)) if and only if for every bounded and continuous function f from T to R, we have
[TABLE]
Note that many authors call this notion “weak convergence” rather than weak-∗ convergence. We will refrain from using the term “weak convergence” in order to be consistent with the functional analysis notation.
The weak-∗ topology on P(T,B(T)) is the coarsest topology which makes the mappings
[TABLE]
continuous over P(T,B(T)), for every bounded and continuous function f from T to R.
III-E Metrization of the weak-∗ topology
If (T,U) is a Polish space (i.e., separable and completely metrizable), the weak-∗ topology on P(T,B(T)) is also Polish [14]. There are many known metrizations for the weak-∗ topology. One metrization that is particularly convenient for us is the Wasserstein metric.
The 1st-Wasserstein distance on P(T,B(T)) is defined as
[TABLE]
where Γ(P,P′) is the collection of all probability measures on T×T with marginals P and P′ on the first and second factors respectively, and d is a metric on T that induces the topology U. Γ(P,P′) is also called the set of couplings of P and P′.
If d is bounded and (T,d) is separable and complete, then W1 metrizes the weak-∗ topology [14]. If (T,U) is compact, then (P(T),W1) is also compact [14].
If D=x,x′∈Tsupd(x,x′) is the diameter of (T,d), then W1(P,P′)≤D∥P−P′∥TV [14]. In other words, the Wasserstein metric is controlled by total variation.
III-F Meta-probability measures
Let X be a finite set. A meta-probability measure on X is a probability measure on the Borel sets of ΔX. It is called a meta-probability measure because it is a probability measure on the space of probability distributions on X.
We denote the set of meta-probability measures on X as MP(X). Clearly, MP(X)=P(ΔX).
A meta-probability measure MP on X is said to be balanced if it satisfies
[TABLE]
where πX is the uniform probability distributions on X.
We denote the set of all balanced meta-probability measures on X as MPb(X). The set of all balanced and finitely supported meta-probability measures on X is denoted as MPbf(X).
IV The space of channels from X to Y
A discrete memoryless channel W is a 3-tuple W=(X,Y,pW) where X is a finite set that is called the input alphabet of W, Y is a finite set that is called the output alphabet of W, and pW:X×Y→[0,1] is a function satisfying ∀x∈X,y∈Y∑pW(x,y)=1.
For every (x,y)∈X×Y, we denote pW(x,y) as W(y∣x), which we interpret as the conditional probability of receiving y at the output, given that x is the input.
Let DMCX,Y be the set of all channels having X as input alphabet and Y as output alphabet.
For every W,W′∈DMCX,Y, define the distance between W and W′ as follows:
[TABLE]
It is easy to check the following properties of dX,Y:
•
0≤dX,Y(W,W′)≤1.
•
dX,Y:DMCX,Y×DMCX,Y→R+ is a metric distance on DMCX,Y.
Throughout this paper, we always associate the space DMCX,Y with the metric distance dX,Y and the metric topology TX,Y induced by it.
For every x∈X, the mapping y→W(y∣x) is a probability distributions on Y. Therefore, every channel W can be seen as a collection of probability distributions on Y, and the collection is indexed by x∈X. This allows us to identify the space DMCX,Y with (ΔY)X=x∈X∏ΔY, where ΔY is the set of probability distributions on Y. It is easy to see that the topology given by the metric dX,Y on DMCX,Y is the same as the product topology on (ΔY)X, which is also the same as the topology inherited from the Euclidean topology of RX×Y by relativization.
It is known that ΔY is a closed and bounded subset of RY. Therefore, ΔY is compact, which implies that (ΔY)X is compact. We conclude that the metric space DMCX,Y≡(ΔY)X is compact. Moreover, since ΔY a convex subset of RY, it is path-connected, hence DMCX,Y≡(ΔY)X is path-connected as well.
If W∈DMCX,Y and V∈DMCY,Z, we define the composition V∘W∈DMCX,Z of W and V as follows:
[TABLE]
It is easy to see that the mapping (W,V)→V∘W from DMCX,Y×DMCY,Z to DMCX,Z is continuous.
For every mapping f:X→Y, define the deterministic channelDf∈DMCX,Y as follows:
[TABLE]
It is easy to see that if f:X→Y and g:Y→Z, then Dg∘Df=Dg∘f.
V Equivalent channels and their representation
Let W∈DMCX,Y and W′∈DMCX,Z be two channels having the same input alphabet. We say that W′ is degraded from W if there exists a channel V∈DMCY,Z such that W′=V∘W. W and W′ are said to be equivalent if each one is degraded from the other. In the rest of this section, we describe one way to check whether two given channels are equivalent.
Let ΔX and ΔY be the space of probability distributions on X and Y respectively. Define PWo∈ΔY as
[TABLE]
This can be interpreted as the probability distribution of the output when the input is uniformly distributed in X. The image of W is the set of output-symbols y∈Y having strictly positive probabilities:
[TABLE]
For every y∈Im(W), define Wy−1∈ΔX as follows:
[TABLE]
Wy−1(x) can be interpreted as the posterior probability of x, given that the output is y, and assuming a uniform prior distribution on the input. In other words, if X is a random variable uniformly distributed in X and Y is the output of the channel W when X is the input, then:
•
PWo(y)=PY(y) for every y∈Y.
•
Wy−1(x)=PX∣Y(x∣y) for every (x,y)∈X×Im(W).
Let (x,y)∈X×Y. If PWo(y)=PY(y)>0, we have
[TABLE]
On the other hand, if PWo(y)=0, then we must have W(y∣x)=0. We conclude that PWo and the collection {Wy−1}y∈Im(W) uniquely determine W.
The Blackwell measure222In an earlier version of this work, I called MPW the posterior meta-probability distribution of W. Maxim Raginsky thankfully brought to my attention the fact that MPW is called Blackwell measure. (denoted MPW) of W is a probability distribution on ΔX having masses PWo(y) on Wy−1 for each y∈Im(W):
[TABLE]
Another way to express MPW is as follows:
[TABLE]
where δWy−1 is a Dirac measure centered at Wy−1∈ΔX.
MPW can be interpreted as follows: after the receiver obtains the output of the channel, he can compute the posterior probabilities of the input as the conditional probability distribution of the input given the output symbol that he received. But before receiving the output symbol, the receiver does not know what he we will receive. He just has different probabilities for different possible output symbols. Therefore, the posterior probability distribution that will be computed by the receiver is itself random, and so we need a meta-probability measure to describe it. MPW is exactly this meta-probability measure.
Since Im(W) is finite, the support of MPW is finite and it consists of all points in ΔX having strictly positive mass:
[TABLE]
The rank of W is the size of the support of its Blackwell measure:
[TABLE]
Notice that for every x∈X, we have
[TABLE]
where (a) follows from the fact that W(y∣x)=0 for every y∈/Im(W). Therefore, we can write
[TABLE]
where πX is the uniform probability distribution on X. This shows that MPW is a balanced meta-probability measure.
The following proposition characterizes the Blackwell measures of DMCs with input alphabet X:
Proposition 1**.**
[15]** A meta-probability measure MP on X is the Blackwell measure of some DMC with input alphabet X if and only if MP is balanced and finitely supported.
Proof.
This proposition is known [15], but we provide a proof for completeness.
The above discussion shows that if MP is the Blackwell measure of some channel with input alphabet X, then it is balanced and finitely supported.
Now assume that MP is balanced and finitely supported, and let Y=supp(MP). Define the channel W∈DMCX,Y as W(p∣x)=∣X∣MP(p)p(x) for every x∈X and every p∈Y=supp(MP). For every x∈X, we have:
[TABLE]
Therefore, W is a valid channel. For every p∈Y, we have
[TABLE]
which implies that Im(W)=Y. For every (x,p)∈X×Y we have:
[TABLE]
Therefore, Wp−1=p for every p∈Y. For every Borel subset B of ΔX, we have:
[TABLE]
We conclude that MPW=MP.
∎
In [3], equivalent representations for binary memoryless symmetric (BMS) channels (namely L, D and G densities) were provided. A necessary and sufficient condition for the degradation of a BMS channel W′ with respect to another BMS channel W was given in [3] in terms of the ∣D∣-densities of W and W′. It immediately follows from this condition that two BMS channels are equivalent if and only if they have the same ∣D∣-densities. One can deduce from this that two BMS channels (with finite output alphabets) are equivalent if and only if they have the same Blackwell measure. The following proposition shows that this is also true for channels with arbitrary (but finite) input and output alphabets:
Proposition 2**.**
[15]** Let X,Y and Z be three finite sets. Two channels W∈DMCX,Y and W′∈DMCX,Z are equivalent if and only if MPW=MPW′.
Proof.
This proposition is known [15], but we provide a proof in Appendix A for completeness.
∎
Corollary 1**.**
If W∈DMCX,Y and rank(W)>∣Z∣, then W is not equivalent to any channel in DMCX,Z.
Proof.
Since rank(W′)=∣supp(MPW′)∣≤∣Z∣ for every W′∈DMCX,Z, it is impossible for W to be equivalent to any channel W′ in DMCX,Z.
∎
Corollary 2**.**
If ∣X∣=1, all channels with input alphabet X are equivalent.
VI Space of equivalent channels from X to Y
VI-A The DMCX,Y(o) space
Let X and Y be two finite sets. Define the relation RX,Y(o) on DMCX,Y as follows:
[TABLE]
It is easy to see that RX,Y(o) is an equivalence relation on DMCX,Y.
Definition 1**.**
The space of equivalent channels with input alphabet X and output alphabet Y is the quotient of the space of channels from X to Y by the equivalence relation:
[TABLE]
We define the topology TX,Y(o) on DMCX,Y(o) as the quotient topology TX,Y/RX,Y(o).
Unless we explicitly state otherwise, we always associate DMCX,Y(o) with the quotient topology TX,Y(o) .
For every W∈DMCX,Y, let W^∈DMCX,Y(o) be the RX,Y(o)-equivalence class containing W.
Lemma 3**.**
The projection mapping Proj:DMCX,Y→DMCX,Y(o) defined as Proj(W)=W^ is continuous and closed.
For every W∈DMCX,Y, W^ is a compact subset of DMCX,Y.
Proof.
Since DMCX,Y is compact, then DMCX,Y(o)=DMCX,Y/RX,Y(o) is compact as well.
Let Proj:DMCX,Y→DMCX,Y(o) be as in Lemma 3. Since Proj is closed and since {W} is closed in DMCX,Y, {W^}=Proj({W}) is closed in DMCX,Y(o). Therefore, W^=Proj−1({W^}) is closed in DMCX,Y because Proj is continuous. Now since DMCX,Y is compact, W^ is compact as well.
∎
Theorem 3**.**
DMCX,Y(o)* is a compact, path-connected and metrizable space.*
Proof.
Since DMCX,Y is compact and path-connected, DMCX,Y(o)=DMCX,Y/RX,Y(o) is compact and path-connected as well.
Since the projection map Proj of Lemma 3 is closed, Lemma 2 implies that the quotient space DMCX,Y(o)=DMCX,Y/RX,Y(o) is upper semi-continuous. On the other hand, Corollary 3 shows that all the members of DMCX,Y(o) are compact in DMCX,Y. Therefore, the conditions of Theorem 1 are satisfied.
Since DMCX,Y is a metric space, it is Hausdorff and regular. Moreover, since it can be seen as a subspace of R∣X∣⋅∣Y∣, it is also second-countable. By Theorem 1 we get that DMCX,Y(o)=DMCX,Y/RX,Y(o) is Hausdorff, regular and second-countable, and from Theorem 2 we conclude that DMCX,Y(o) is separable and metrizable.
∎
VI-B Canonical embedding and canonical identification
Let X,Y1 and Y2 be three finite sets such that ∣Y1∣≤∣Y2∣. We will show that there is a canonical embedding from DMCX,Y1(o) to DMCX,Y2(o). In other words, there exists an explicitly constructable compact subset A of DMCX,Y2(o) such that A is homeomorphic to DMCX,Y1(o). A and the homeomorphism depend only on X,Y1 and Y2 (this is why we say that they are canonical). Moreover, we can show that A depends only on ∣Y1∣, X and Y2.
Lemma 4**.**
For every W∈DMCX,Y1 and every injection f from Y1 to Y2, W is equivalent to Df∘W.
Proof.
Clearly Df∘W is degraded from W. Now let f′ be any mapping from Y2 to Y1 such that f′(f(y1))=y1 for every y1∈Y1. We have W=(Df′∘Df)∘W=Df′∘(Df∘W), and so W is also degraded from Df∘W.
∎
Corollary 4**.**
For every W,W′∈DMCX,Y1 and every two injections f,g from Y1 to Y2, we have:
[TABLE]
Proof.
Since W is equivalent to Df∘W and W′ is equivalent to Dg∘W′, then W is equivalent to W′ if and only if Df∘W is equivalent to Dg∘W′.
∎
For every W∈DMCX,Y1, we denote the RX,Y1(o)-equivalence class of W as W^, and for every W∈DMCX,Y2, we denote the RX,Y2(o)-equivalence class of W as W~.
Proposition 3**.**
Let f:Y1→Y2 be any fixed injection between Y1 and Y2. Define the mapping F:DMCX,Y1(o)→DMCX,Y2(o) as
F(W^)=Df∘W′=Proj2(Df∘W′), where W′∈W^ and Proj2:DMCX,Y2→DMCX,Y2(o) is the projection onto the RX,Y2(o)-equivalence classes. We have:
•
F* is well defined, i.e., Proj2(Df∘W′) does not depend on W′∈W^.*
•
F* is a homeomorphism between DMCX,Y1(o) and F\big{(}\operatorname*{DMC}_{\mathcal{X},\mathcal{Y}_{1}}^{(o)}\big{)}.*
•
F* does not depend on f, i.e., F depends only on X,Y1 and Y2.*
•
F\big{(}\operatorname*{DMC}_{\mathcal{X},\mathcal{Y}_{1}}^{(o)}\big{)}* depends only on ∣Y1∣, X and Y2.*
•
For every W′∈W^ and every W′′∈F(W^), W′ is equivalent to W′′.
Proof.
Corollary 4 implies that Proj2(Df∘W)=Proj2(Df∘W′) if and only if WRX,Y1(o)W′. Therefore, Proj2(Df∘W′) does not depend on W′∈W^, hence F is well defined. Corollary 4 also shows that Proj2(Df∘W′) does not depend on the particular choice of the injection f, hence it is canonical (i.e., it depends only on X,Y1 and Y2).
On the other hand, the mapping W→Df∘W is a continuous mapping from DMCX,Y1 to DMCX,Y2, and Proj2 is continuous. Therefore, the mapping W→Proj2(Df∘W) is a continuous mapping from DMCX,Y1 to DMCX,Y2(o). Now since Proj2(Df∘W) depends only on the RX,Y1(o)-equivalence class W^ of W, Lemma 1 implies that F is continuous. Moreover, we can see from Corollary 4 that F is an injection.
For every closed subset B of DMCX,Y1(o), B is compact since DMCX,Y1(o) is compact, hence F(B) is compact because F is continuous. This implies that F(B) is closed in DMCX,Y2(o) since DMCX,Y2(o) is Hausdorff (as it is metrizable). Therefore, F is a closed mapping.
Now since F is an injection that is both continuous and closed, we can deduce that F is a homeomorphism between DMCX,Y1(o) and F\big{(}\operatorname*{DMC}_{\mathcal{X},\mathcal{Y}_{1}}^{(o)}\big{)}\subset\operatorname*{DMC}_{\mathcal{X},\mathcal{Y}_{2}}^{(o)}.
We would like now to show that F\big{(}\operatorname*{DMC}_{\mathcal{X},\mathcal{Y}_{1}}^{(o)}\big{)} depends only on ∣Y1∣, X and Y2. Let Y1′ be a finite set such that ∣Y1∣=∣Y1′∣. For every W∈DMCX,Y1′, let W∈DMCX,Y1′(o) be the RX,Y1′(o)-equivalence class of W.
Let g:Y1′→Y1 be a fixed bijection from Y1′ to Y1 and let f′=f∘g. Define F′:DMCX,Y1′(o)→DMCX,Y2(o) as F′(W)=Df′∘W′=Proj2(Df′∘W′), where W′∈W. As above, F′ is well defined, and it is a homeomorphism from DMCX,Y1′(o) to F^{\prime}\big{(}\operatorname*{DMC}_{\mathcal{X},\mathcal{Y}_{1}^{\prime}}^{(o)}\big{)}. We want to show that F^{\prime}\big{(}\operatorname*{DMC}_{\mathcal{X},\mathcal{Y}_{1}^{\prime}}^{(o)}\big{)}=F\big{(}\operatorname*{DMC}_{\mathcal{X},\mathcal{Y}_{1}}^{(o)}\big{)}. For every W∈DMCX,Y1′(o), let W′∈W. We have
[TABLE]
Since this is true for every W∈DMCX,Y1′(o), we deduce that F^{\prime}\big{(}\operatorname*{DMC}_{\mathcal{X},\mathcal{Y}_{1}^{\prime}}^{(o)}\big{)}\subset F\big{(}\operatorname*{DMC}_{\mathcal{X},\mathcal{Y}_{1}}^{(o)}\big{)}. By exchanging the roles of Y1 and Y1′ and using the fact that f=f′∘g−1, we get F\big{(}\operatorname*{DMC}_{\mathcal{X},\mathcal{Y}_{1}}^{(o)}\big{)}\subset F^{\prime}\big{(}\operatorname*{DMC}_{\mathcal{X},\mathcal{Y}_{1}^{\prime}}^{(o)}\big{)}. We conclude that F\big{(}\operatorname*{DMC}_{\mathcal{X},\mathcal{Y}_{1}}^{(o)}\big{)}=F^{\prime}\big{(}\operatorname*{DMC}_{\mathcal{X},\mathcal{Y}_{1}^{\prime}}^{(o)}\big{)}, which means that F\big{(}\operatorname*{DMC}_{\mathcal{X},\mathcal{Y}_{1}}^{(o)}\big{)} depends only on ∣Y1∣, X and Y2.
Finally, for every W′∈W^ and every W′′∈F(W^)=Df∘W′, W′′ is equivalent to Df∘W′ and Df∘W′ is equivalent to W′ (by Lemma 4), hence W′′ is equivalent to W′.
∎
Corollary 5**.**
If ∣Y1∣=∣Y2∣, there exists a canonical homeomorphism from DMCX,Y1(o) to DMCX,Y2(o) depending only on X,Y1 and Y2.
Proof.
Let f be a bijection from Y1 to Y2. Define the mapping F:DMCX,Y1(o)→DMCX,Y2(o) as
F(W^)=Df∘W′=Proj2(Df∘W′), where W′∈W^ and Proj2:DMCX,Y2→DMCX,Y2(o) is the projection onto the RX,Y2(o)-equivalence classes.
Also, define the mapping F′:DMCX,Y2(o)→DMCX,Y1(o) as
F′(V~)=Df−1∘V′=Proj1(Df−1∘V′), where V′∈V~ and Proj1:DMCX,Y1→DMCX,Y1(o) is the projection onto the RX,Y1(o)-equivalence classes.
Proposition 3 shows that F and F′ are well defined.
For every W∈DMCX,Y1, we have:
[TABLE]
where (a) follows from the fact that W∈W^ and (b) follows from the fact that Df∘W∈Df∘W.
We can similarly show that F(F′(V~))=V~ for every V~∈DMCX,Y2(o). Therefore, both F and F′ are bijections. Proposition 3 now implies that F is a homeomorphism from DMCX,Y1(o) to F\big{(}\operatorname*{DMC}_{\mathcal{X},\mathcal{Y}_{1}}^{(o)}\big{)}=\operatorname*{DMC}_{\mathcal{X},\mathcal{Y}_{2}}^{(o)}. Moreover, F depends only on X,Y1 and Y2.
∎
Corollary 5 allows us to identify DMCX,Y1(o) with DMCX,Y2(o) whenever ∣Y1∣=∣Y2∣. In the rest of this paper, we identify DMCX,Y(o) with DMCX,[n](o) through the canonical identification, where n=∣Y∣ and [n]={1,…,n}.
Moreover, for every 1≤n≤m, Proposition 3 allows us to identify DMCX,[n](o) with the canonical subspace of DMCX,[m](o) that is homeomorphic to DMCX,[n](o). In the rest of this paper, we consider that DMCX,[n](o) is a compact subspace of DMCX,[m](o).
Intuitively, DMCX,[n](o) has a “lower dimension” compared to DMCX,[m](o). So one expects that the interior of DMCX,[n](o) in (DMCX,[m](o),TX,[m](o)) is empty if m>n. The following proposition shows that this intuition is accurate.
Proposition 4**.**
If ∣X∣≥2, then for every 1≤n<m, the interior of DMCX,[n](o) in (DMCX,[m](o),TX,[m](o)) is empty.
We would like to form the space of all equivalent channels having the same input alphabet X. The previous section showed that if ∣Y1∣=∣Y2∣, there is a canonical identification between DMCX,Y1(o) and DMCX,Y2(o). This shows that if we are interested in equivalent channels, it is sufficient to study the spaces DMCX,[n] and DMCX,[n](o) for every n≥1. Define the space
[TABLE]
The subscript ∗ indicates that the output alphabets of the considered channels are arbitrary but finite.
We define the equivalence relation RX,∗(o) on DMCX,∗ as follows:
[TABLE]
Definition 2**.**
The space of equivalent channels with input alphabet X is the quotient of the space of channels with input alphabet X by the equivalence relation:
[TABLE]
For every n≥1 and every W,W′∈DMCX,[n], we have WRX,∗(o)W′ if and only if WRX,[n](o)W′ by definition. Therefore, DMCX,[n]/RX,∗(o) can be canonically identified with DMCX,[n]/RX,[n](o)=DMCX,[n](o). But since we identified DMCX,[n](o) to its image through the canonical embedding in DMCX,[m](o) for every m≥n, we have to make sure that these identifications are consistent with each other.
Remember that for every m≥n≥1 and every W∈DMCX,[n], we identified W^ with Df∘W, where f is any injection from [n] to [m], W^ is the RX,[n](o)-equivalence class of W and Df∘W is the RX,[m](o)-equivalence class of Df∘W. Since Df∘W is equivalent to W (by Lemma 4), W is RX,∗(o)-equivalent to Df∘W for every W∈DMCX,[n](o). We conclude that identifying DMCX,[n](o) to its image through the canonical embedding in DMCX,[m](o) for every m≥n≥1 is consistent with identifying DMCX,[n]/RX,∗(o) to DMCX,[n](o) for every n≥1. Hence, we can write
[TABLE]
For any W,W′∈DMCX,∗, Proposition 2 shows that WRX,∗(o)W′ if and only if MPW=MPW′. Therefore, for every W^∈DMCX,∗(o), we can define the Blackwell measure of W^ as MPW^:=MPW′ for any W′∈W^. We also define the rank of W^ as rank(W^)=∣supp(MPW^)∣. Due to Proposition 2, we have
[TABLE]
A subset A of DMCX,∗(o) is said to be rank-bounded if there exists n≥1 such that A⊂DMCX,[n](o). A is rank-unbounded if it is not rank-bounded.
VII-A Natural topologies on DMCX,∗(o)
Since DMCX,∗(o) is the quotient of DMCX,∗ and since DMCX,∗ was not given any topology, there is no “standard topology” on DMCX,∗(o).
However, there are many properties that one may require from any “reasonable” topology on DMCX,∗(o). For example, one may require the continuity of all mappings that are relevant to information theory such as capacity, mutual information, probability of error of any fixed code, optimal probability of error of a given rate and blocklength, channel sums and products, etc … The continuity of these mappings under different topologies on DMCX,∗(o) is studied in [7].
In this paper, we focus on one particular requirement that we consider the most basic property required from any “acceptable” topology on DMCX,∗(o):
Definition 3**.**
A topology T on DMCX,∗(o) is said to be natural if it induces the quotient topology TX,[n](o) on DMCX,[n](o) for every n≥1.
The reason why we consider such topology as natural is because DMCX,[n](o) is subset of DMCX,∗(o) and the quotient topology TX,[n](o) is the “standard” and “most natural” topology on DMCX,[n](o). Therefore, we do not want to induce any non-standard topology on DMCX,[n](o) by relativization.
Before discussing any particular natural topology, we would like to discuss a few properties that are common to all natural topologies.
Proposition 5**.**
Every natural topology is σ-compact, separable and path-connected.
Proof.
Since DMCX,∗(o) is the countable union of compact and separable subspaces (namely {DMCX,[n](o)}n≥1), DMCX,∗(o) is σ-compact and separable.
On the other hand, since n≥1⋂DMCX,[n](o)=DMCX,[1](o)=\o and since DMCX,[n](o) is path-connected for every n≥1, the union DMCX,∗(o)=n≥1⋃DMCX,[n](o) is path-connected.
∎
Proposition 6**.**
If ∣X∣≥2 and T is a natural topology, every open set is rank-unbounded.
Proof.
Assume to the contrary that there exists a non-empty open set U∈T such that U⊂DMCX,[n](o) for some n≥1. U∩DMCX,[n+1](o) is open in DMCX,[n+1](o) because T is natural. On the other hand, U∩DMCX,[n+1](o)⊂U⊂DMCX,[n](o). Proposition 4 now implies that U∩DMCX,[n+1](o)=\o. Therefore,
[TABLE]
which is a contradiction.
∎
Corollary 6**.**
If ∣X∣≥2 and T is a natural topology, then for every n≥1, the interior of DMCX,[n](o) in (DMCX,∗(o),T) is empty.
Proposition 7**.**
If ∣X∣≥2 and T is a Hausdorff natural topology, then (DMCX,∗(o),T) is not a Baire space.
Proof.
Fix n≥1. Since T is natural, DMCX,[n](o) is a compact subset of (DMCX,∗(o),T). But T is Hausdorff, so DMCX,[n](o) is a closed subset of (DMCX,∗(o),T). Therefore, DMCX,∗(o)∖DMCX,[n](o) is open.
On the other hand, Corollary 6 shows that the interior of DMCX,[n](o) in (DMCX,∗(o),T) is empty. Therefore, DMCX,∗(o)∖DMCX,[n](o) is dense in (DMCX,∗(o),T).
Now since
[TABLE]
and since DMCX,∗(o)∖DMCX,[n](o) is open and dense in (DMCX,∗(o),T) for every n≥1, we conclude that (DMCX,∗(o),T) is not a Baire space.
∎
Corollary 7**.**
If ∣X∣≥2, no natural topology on DMCX,∗(o) can be completely metrizable.
Proof.
The corollary follows from Proposition 7 and the fact that every completely metrizable topology is both Hausdorff and Baire.
∎
Proposition 8**.**
If ∣X∣≥2 and T is a Hausdorff natural topology, then (DMCX,∗(o),T) is not locally compact anywhere, i.e., for every W^∈DMCX,∗(o), there is no compact neighborhood of W^ in (DMCX,∗(o),T).
Proof.
Assume to the contrary that there exists a compact neighborhood K of W^. There exists an open set U such that W^∈U⊂K.
Since K is compact and Hausdorff, it is a Baire space. Moreover, since U is an open subset of K, U is also a Baire space.
Fix n≥1. Since the interior of DMCX,[n](o) in (DMCX,∗(o),T) is empty, the interior of U∩DMCX,[n](o) in U is also empty. Therefore, U∖DMCX,[n](o) is dense in U. On the other hand, since T is natural, DMCX,[n](o) is compact which implies that it is closed because T is Hausdorff. Therefore, U∖DMCX,[n](o) is open in U. Now since
[TABLE]
and since U∖DMCX,[n](o) is open and dense in U for every n≥1, U is not Baire, which is a contradiction. Therefore, there is no compact neighborhood of W^ in (DMCX,∗(o),T).
∎
VIII Strong topology on DMCX,∗(o)
The first natural topology that we study is the strong topologyTs,X,∗(o) on DMCX,∗(o), which is the finest natural topology.
Since the spaces {DMCX,[n]}n≥1 are disjoint and since there is no a priori way to (topologically) compare channels in DMCX,[n] with channels in DMCX,[n′] for n=n′, the “most natural” topology that we can define on DMCX,∗ is the disjoint union topology Ts,X,∗:=n≥1⨁TX,[n]. Clearly, the space (DMCX,∗,Ts,X,∗) is disconnected. Moreover, Ts,X,∗ is metrizable because it is the disjoint union of metrizable spaces. It is also σ-compact because it is the union of countably many compact spaces.
We added the subscript s to emphasize the fact that Ts,X,∗ is a strong topology (remember that the disjoint union topology is the finest topology that makes the canonical injections continuous).
Definition 4**.**
We define the strong topology Ts,X,∗(o) on DMCX,∗(o) as the quotient topology Ts,X,∗/RX,∗(o).
We call open and closed sets in (DMCX,∗(o),Ts,X,∗(o)) as strongly open and strongly closed sets respectively.
Let Proj:DMCX,∗→DMCX,∗(o) be the projection onto the RX,∗(o)-equivalence classes, and for every n≥1 let Projn:DMCX,[n]→DMCX,[n](o) be the projection onto the RX,[n](o)-equivalence classes. Due to the identifications that we made in Section VII, we have Proj(W)=Projn(W) for every W∈DMCX,[n]. Therefore, for every U⊂DMCX,∗(o), we have
[TABLE]
Hence,
[TABLE]
where (a) and (c) follow from the properties of the quotient topology, and (b) follows from the properties of the disjoint union topology.
We conclude that U⊂DMCX,∗(o) is strongly open in DMCX,∗(o) if and only if U∩DMCX,[n](o) is open in DMCX,[n](o) for every n≥1. This shows that the topology on DMCX,[n](o) that is inherited from (DMCX,∗(o),Ts,X,∗(o)) is exactly TX,[n](o). Therefore, Ts,X,∗(o) is a natural topology. On the other hand, if T is an arbitrary natural topology and U∈T, then U∩DMCX,[n](o) is open in DMCX,[n](o) for every n≥1, so U∈Ts,X,∗(o). We conclude that Ts,X,∗(o) is the finest natural topology.
We can also characterize the strongly closed subsets of DMCX,∗(o) in terms of the closed sets of the DMCX,[n](o) spaces:
[TABLE]
Since DMCX,[n](o) is metrizable for every n≥1, it is also normal. We can use this fact to prove that the strong topology on DMCX,∗(o) is normal:
The following theorem shows that the strong topology satisfies many desirable properties.
Theorem 4**.**
(DMCX,∗(o),Ts,X,∗(o))* is a compactly generated, sequential and T4 space.*
Proof.
Since (DMCX,∗,Ts,X,∗) is metrizable, it is sequential. Therefore, (DMCX,∗(o),Ts,X,∗(o)), which is the quotient of a sequential space, is sequential.
Let us now show that DMCX,∗(o) is T4. Fix W^∈DMCX,∗(o). For every n≥1, {W^}∩DMCX,[n](o) is either {W^} or \o depending on whether W^∈DMCX,[n](o) or not. Since DMCX,[n](o) is metrizable, it is T1 and so singletons are closed in DMCX,[n](o). We conclude that in all cases, {W^}∩DMCX,[n](o) is closed in DMCX,[n](o) for every n≥1. Therefore, {W^} is strongly closed in DMCX,∗(o). This shows that (DMCX,∗(o),Ts,X,∗(o)) is T1. On the other hand, Lemma 5 shows that (DMCX,∗(o),Ts,X,∗(o)) is normal. This means that (DMCX,∗(o),Ts,X,∗(o)) is T4, which implies that it is Hausdorff.
Now since (DMCX,∗,Ts,X,∗) is metrizable, it is compactly generated. On the other hand, the quotient space (DMCX,∗(o),Ts,X,∗(o)) was shown to be Hausdorff. We conclude that (DMCX,∗(o),Ts,X,∗(o)) is compactly generated.
∎
Corollary 8**.**
If ∣X∣≥2, (DMCX,∗(o),Ts,X,∗(o)) is not locally compact anywhere.
Proof.
Since Ts,X,∗(o) is a natural Hausdorff topology, Proposition 8 implies that Ts,X,∗(o) is not locally compact anywhere.
∎
Although (DMCX,∗,Ts,X,∗) is second-countable (because it is a σ-compact metrizable space), the quotient space (DMCX,∗(o),Ts,X,∗(o)) is not second-countable. In fact, we will show later that (DMCX,∗(o),Ts,X,∗(o)) fails to be first-countable (and hence it is not metrizable). This is one manifestation of the strength of the topology Ts,X,∗(o). In order to show that (DMCX,∗(o),Ts,X,∗(o)) is not first-countable, we need to characterize the converging sequences in (DMCX,∗(o),Ts,X,∗(o)).
A sequence (W^n)n≥1 in DMCX,∗(o) is said to be rank-bounded if rank(W^n) is bounded. (W^n)n≥1 is rank-unbounded if it is not bounded.
The following proposition shows that every rank-unbounded sequence does not converge in (DMCX,∗(o),Ts,X,∗(o)).
Proposition 9**.**
A sequence (W^n)n≥0 converges in (DMCX,∗(o),Ts,X,∗(o)) if and only if there exists m≥1 such that W^n∈DMCX,[m](o) for every n≥0, and (W^n)n≥0 converges in (DMCX,[m](o),TX,[m](o)).
Proof.
Assume that a sequence (W^n)n≥0 in DMCX,∗(o) is rank-unbounded. This cannot happen unless ∣X∣≥2. In order to show that (W^n)n≥0 does not converge, it is sufficient to show that there exists a subsequence of (W^n)n≥0 which does not converge.
Let (W^nk)k≥0 be any subsequence of (W^n)n≥0 where the rank strictly increases, i.e., rank(Wnk)<rank(Wnk′) for every 0≤k<k′. We will show that (W^nk)k≥0 does not converge.
Assume to the contrary that (W^nk)k≥0 converges to W^∈DMCX,∗(o). Define the set
[TABLE]
For every m≥1, the set A∩DMCX,[m](o) contains finitely many points. This means that A∩DMCX,[m](o) is a finite union of singletons (which are closed in DMCX,[m](o)), hence A∩DMCX,[m](o) is closed in DMCX,[m](o) for every m≥1. Therefore A is closed in (DMCX,∗(o),Ts,X,∗(o)).
Now define U=DMCX,∗(o)∖A. Since A is strongly closed, U is strongly open. Moreover, U contains W^, so U is a neighborhood of W^. Therefore, there exists k0≥0 such that W^nk∈U for every k≥k0. Now since the rank of (W^nk)k≥0 strictly increases, we can find k≥k0 such that rank(W^nk)>rank(W^). This means that W^nk=W^ and so W^nk∈A. Therefore, W^nk∈/U which is a contradiction.
We conclude that every converging sequence in (DMCX,∗(o),Ts,X,∗(o)) must be rank-bounded.
Now let (W^n)n≥0 be a rank-bounded sequence in DMCX,∗(o), i.e., there exists m≥1 such that W^n∈DMCX,[m](o) for every n≥0. If (W^n)n≥0 converges in (DMCX,∗(o),Ts,X,∗(o)) then it converges in DMCX,[m](o) since DMCX,[m](o) is strongly closed.
Conversely, assume that (W^n)n≥0 converges in (DMCX,[m](o),TX,[m](o)) to W^∈DMCX,[m](o). Let O be any neighborhood of W^ in (DMCX,∗(o),Ts,X,∗(o)). There exists a strongly open set U such that W^∈U⊂O. Since U∩DMCX,[m](o) is open in (DMCX,[m](o),TX,[m](o)), there exists n0>0 such that W^n∈U∩DMCX,[m](o) for every n≥n0. This implies that W^n∈O for every n≥n0. Therefore (W^n)n≥0 converges to W^ in (DMCX,∗(o),Ts,X,∗(o)).
∎
Corollary 9**.**
If ∣X∣≥2, (DMCX,∗(o),Ts,X,∗(o)) is not first-countable anywhere, i.e., for every W^∈DMCX,∗(o), there is no countable neighborhood basis of W^.
Proof.
Fix W^∈DMCX,∗(o) and assume to the contrary that W^ admits a countable neighborhood basis {On}n≥1 in (DMCX,∗(o),Ts,X,∗(o)). For every n≥1, let Un′ be a strongly open set such that W^∈Un′⊂On. Define Un=i=1⋂nUn′. Un is strongly open because it is the intersection of finitely many strongly open sets. Moreover, Un⊂Om for every n≥m.
For every n≥1, Proposition 6 implies that Un (which is non-empty and strongly open) is rank-unbounded, so it cannot be contained in DMCX,[n](o). Hence there exists W^n∈Un such that W^n∈/DMCX,[n](o).
Since W^n∈/DMCX,[n](o), we have rank(W^n)>n for every n≥1. Therefore, (W^n)n≥1 is rank-unbounded. Proposition 9 implies that (W^n)n≥1 does not converge in (DMCX,∗(o),Ts,X,∗(o)).
Now let O be a neighborhood of W^ in (DMCX,∗(o),Ts,X,∗(o)). Since {On}n≥1 is a neighborhood basis for W^, there exists n0≥1 such that On0⊂O. For every n≥n0, we have W^n∈Un⊂On0⊂O. This means that (W^n)n≥1 converges to W^ in (DMCX,∗(o),Ts,X,∗(o)) which is a contradiction. Therefore, W^ does not admit a countable neighborhood basis in (DMCX,∗(o),Ts,X,∗(o)).
∎
VIII-A Compact subspaces of (DMCX,∗(o),Ts,X,∗(o))
It is well known that a compact subset of R is compact if and only if it is closed and bounded. The following proposition shows that a similar statement holds for (DMCX,∗(o),Ts,X,∗(o)).
Proposition 10**.**
A subspace of (DMCX,∗(o),Ts,X,∗(o)) is compact if and only if it is rank-bounded and strongly closed.
Proof.
If ∣X∣=1, all channels are equivalent to each other and so DMCX,∗(o)=DMCX,[1](o) consists of a single point. Therefore, all subsets of DMCX,∗(o) are rank-bounded, compact and strongly closed.
Assume now that ∣X∣≥2.
Let A be a subspace of (DMCX,∗(o),Ts,X,∗(o)). If A is rank-bounded and strongly closed, then there exists n≥1 such that A⊂DMCX,[n](o). Since A is strongly closed, then A=A∩DMCX,[n](o) is closed in DMCX,[n](o) which is compact. Therefore, A is compact.
Now let A be a compact subspace of (DMCX,∗(o),Ts,X,∗(o)). Since (DMCX,∗(o),Ts,X,∗(o)) is Hausdorff, A is strongly closed. It remains to show that A is rank-bounded.
Assume to the contrary that A is rank-unbounded. We can construct a sequence (W^n)n≥0 in A where the rank is strictly increasing, i.e., rank(W^n)<rank(W^n′) for every 0≤n<n′. Since the rank of (W^n)n≥0 is strictly increasing, every subsequence of (W^n)n≥0 is rank-unbounded. Proposition 9 implies that every subsequence of (W^n)n≥0 does not converge in (DMCX,∗(o),Ts,X,∗(o)). On the other hand, we have:
•
A is countably compact because it is compact.
•
Since A is strongly closed and since (DMCX,∗(o),Ts,X,∗(o)) is a sequential space, A is sequential.
•
A is Hausdorff because (DMCX,∗(o),Ts,X,∗(o)) is Hausdorff.
Now since every countably compact sequential Hausdorff space is sequentially compact [12], A must be sequentially compact. Therefore, (W^n)n≥0 has a converging subsequence which is a contradiction. We conclude that A must be rank-bounded.
∎
IX The noisiness metric on DMC spaces
Theorem 3 implies that DMCX,[n](o) is metrizable for every n≥1. One might ask whether the spaces DMCX,[n](o) are “simultaneously metrizable” in the sense that we can define a metric dn on DMCX,[n](o) for every n≥1 in such a way that dn is the restriction of dn+1 for every n≥1. If this is the case, we can then define a metric on DMCX,∗(o)=n≥1⋃DMCX,[n](o) as d(W^,W^′)=dn(W^,W^′) for any n≥1 satisfying W^,W^′∈DMCX,[n](o). In this section we will show that such metrics can be constructed.
IX-A Noisiness metric on DMCX,Y(o)
For every m≥1, let Δ[m]×X be the space of probability distributions on [m]×X.
Let Y be a finite set and let W∈DMCX,Y. For every p∈Δ[m]×X, define Pc(p,W) as follows:
[TABLE]
Pc(p,W) can be interpreted as follows: let (U,X) be a pair of random variables distributed according to p, send X through the channel W, and let Y be the output of W in such a way that U−X−Y is a Markov chain. Let U^ be the estimate of U obtained by applying a random decoder D∈DMCY,[m]. In this interpretation, p can be seen as a random encoder. The probability of correctly guessing U by using the decoder D is given by
[TABLE]
Therefore, Pc(p,W) is the optimal probability of correctly guessing U from Y. Note that we can take the supremum in (2) over only deterministic channels D∈DMCY,[m] because we can always choose an optimal decoder that is deterministic.
It is well known that if W is degraded from W′, then Pc(p,W)≤Pc(p,W′) for every p∈Δ[m]×X and every m≥1. It was shown in [16] that the converse is also true. Therefore, W is equivalent to W′ if and only if Pc(p,W)=Pc(p,W′) for every p∈Δ[m]×X and every m≥1. This shows that the quantity Pc(p,W) depends only on the RX,Y(o)-equivalence class of W. Therefore, if W^∈DMCX,Y(o), we can define Pc(p,W^):=Pc(p,W′) for any W′∈W^.
Define the noisiness distancedX,Y(o):DMCX,Y(o)×DMCX,Y(o)→R+ as follows:
[TABLE]
It is easy to see that 0≤dX,Y(o)(W^1,W^2)≤1 for every W^1,W^2∈DMCX,Y(o). Moreover, we have:
•
dX,Y(o)(W^,W^)=0 for every W^∈DMCX,Y(o).
•
For every W^1,W^2∈DMCX,Y(o), if dX,Y(o)(W^1,W^2)=0, then Pc(p,W^1)=Pc(p,W^2) for every p∈Δ[m]×X and every m≥1, which implies that the channels in W^1 are equivalent to the channels in W^2, hence W^1=W^2.
•
dX,Y(o)(W^1,W^2)=dX,Y(o)(W^2,W^1) for every W^1,W^2∈DMCX,Y(o).
•
dX,Y(o)(W^1,W^3)≤dX,Y(o)(W^1,W^2)+dX,Y(o)(W^2,W^3) for every W^1,W^2,W^3∈DMCX,Y(o).
This shows that dX,Y(o) is a metric on DMCX,Y(o). dX,Y(o) is called the noisiness metric because it compares the “noisiness” of W^1 with that of W^2: if Pc(p,W^1) is close to Pc(p,W^2) for every random encoder p, then W^1 and W^2 have close “noisiness levels”.
A natural question to ask is whether the metric topology on DMCX,Y(o) that is induced by dX,Y(o) is the same as the quotient topology TX,Y(o) that we defined in Section VI-A. To answer this question, we need the following lemma.
Lemma 6**.**
For every W1,W2∈DMCX,Y, we have:
[TABLE]
where W^1 and W^2 are the RX,Y(o)-equivalence classes of W1 and W2 respectively.
(DMCX,Y(o),dX,Y(o))* and (DMCX,Y(o),TX,Y(o)) are topologically equivalent.*
Proof.
Consider the projection mapping Proj:DMCX,Y→DMCX,Y(o) defined as Proj(W)=W^, where W^ is the RX,Y(o)-equivalence class of W.
Lemma 6 implies that Proj is a continuous mapping from (DMCX,Y,dX,Y) to (DMCX,Y(o),dX,Y(o)). Now since Proj(W)=Proj(W′) whenever WRX,Y(o)W′, Lemma 1 implies that the identity mapping id:DMCX,Y(o)→DMCX,Y(o) is continuous from (DMCX,Y(o),TX,Y(o)) to (DMCX,Y(o),dX,Y(o)). We have:
•
For every U⊂DMCX,Y(o) that is open in (DMCX,Y(o),dX,Y(o)), U=id−1(U)∈TX,Y(o) because id is a continuous mapping from (DMCX,Y(o),TX,Y(o)) to (DMCX,Y(o),dX,Y(o)).
•
For every U∈TX,Y(o), the set DMCX,Y(o)∖U is closed in (DMCX,Y(o),TX,Y(o)) which is compact. Therefore, DMCX,Y(o)∖U is a compact subset of (DMCX,Y(o),TX,Y(o)). Now since id is continuous from (DMCX,Y(o),TX,Y(o)) to (DMCX,Y(o),dX,Y(o)), DMCX,Y(o)∖U=id(DMCX,Y(o)∖U) is a compact subset of (DMCX,Y(o),dX,Y(o)) which is Hausdorff (because it is metric). This shows that DMCX,Y(o)∖U is closed in (DMCX,Y(o),dX,Y(o)), which implies that U is open in (DMCX,Y(o),dX,Y(o)).
We conclude that U⊂DMCX,Y(o) is open in (DMCX,Y(o),dX,Y(o)) if and only if it is open in (DMCX,Y(o),TX,Y(o)).
∎
Corollary 10**.**
(DMCX,Y(o),dX,Y(o))* is a compact path-connected metric space.*
The reader might be wondering why we considered and studied the quotient topology TX,Y(o) while it is possible to explicitly define a metric on the space DMCX,Y(o). There are two reasons:
•
The definition of dX,Y(o) does not seem to be intuitive at the first sight and it is not clear why one would adopt it as a standard metric on DMCX,Y(o). Just being a metric is not convincing enough. On the other hand, the existence of a natural standard topology on DMCX,Y makes the quotient topology the most natural starting point.
•
If one wants to show that a mapping f:DMCX,Y(o)→S is continuous from (DMCX,Y(o),dX,Y(o)) to a topological space (S,V), it is much easier to prove it through the quotient topology TX,Y(o) rather than proving it directly using the metric dX,Y(o). Therefore, it is important to show the topological equivalence between (DMCX,Y(o),dX,Y(o)) and (DMCX,Y(o),TX,Y(o)).
It is worth mentioning that in the proof of Proposition 11, the only topological property of (DMCX,Y(o),TX,Y(o)) that we used is its compactness. This means that we do not need Lemma 3 to prove Theorem 3. An alternative proof of Theorem 3 would be to show the compactness and path-connectedness by inheriting those properties from DMCX,Y, and then show that (DMCX,Y(o),TX,Y(o)) is topologically equivalent to (DMCX,Y(o),dX,Y(o)) as in Proposition 11.
The main reason why we restricted ourselves to topological methods in Section VI-A is because they might be useful if one wants to generalize our results to spaces of non-discrete channels. It might not be easy to find an explicit metric for those spaces, or even worse, those spaces might fail to be metrizable. Therefore, one might want to prove weaker topological properties such as being Hausdorff and/or regular. In such cases, the methods of Section VI-A might be useful.
IX-B Noisiness metric on DMCX,∗(o)
For every W^1,W^2∈DMCX,∗(o), define the noisiness metric on DMCX,∗(o) as follows:
[TABLE]
dX,∗(o)(W^,W^′) is well defined because dX,[n](o)(W^,W^′) does not depend on n≥1 as long as W^,W^′∈DMCX,[n](o). We can also express dX,∗(o) as follows:
[TABLE]
It is easy to see that dX,∗(o) is a metric on DMCX,∗(o). Let TX,∗(o) be the metric topology on DMCX,∗(o) that is induced by dX,∗(o). We call TX,∗(o) the noisiness topology on DMCX,∗(o).
Clearly, TX,∗(o) is natural because the restriction of dX,∗(o) on DMCX,[n](o) is exactly dX,[n](o), and the topology induced by dX,[n](o) is TX,[n](o). If ∣X∣≥2, Proposition 8 and Corollary 7 imply that (DMCX,∗(o),dX,∗(o)) is not complete nor locally compact.
Since Ts,X,∗(o) is the finest natural topology, Ts,X,∗(o) is finer than TX,∗(o). On the other hand, if ∣X∣≥2, TX,∗(o) is metrizable and Ts,X,∗(o) is not (because it is not first-countable). Therefore, if ∣X∣≥2, the strong topology Ts,X,∗(o) is strictly finer than the noisiness topology TX,∗(o).
It is worth mentioning that Propositions 9 and 10 do not hold for (DMCX,∗(o),TX,∗(o)). It is easy to find a rank-unbounded sequence {W^n}n≥0 which converges in (DMCX,∗(o),TX,∗(o)) to a point W^∈DMCX,∗(o). The set {W^n:n≥0}∪{W^} is clearly compact and rank-unbounded.
X Topologies from Blackwell measures
We saw in Section VIII that for every W^∈DMCX,∗(o), a Blackwell measure MPW^ on ΔX is defined. Moreover, Proposition 2 implies that W^ is uniquely determined by MPW^. Therefore, each RX,∗(o)-equivalence class in DMCX,∗(o) can be identified with its Blackwell measure. On the other hand, Proposition 1 shows that the collection of Blackwell measures of the channels with input alphabet X is the same as the collection of balanced and finitely supported meta-probability measures on X.
Therefore, the mapping W^→MPW^ is a bijection from DMCX,∗(o) to MPbf(X). We call this mapping the canonical bijection from DMCX,∗(o) to MPbf(X). Similarly, the inverse mapping is called the canonical bijection from MPbf(X) to DMCX,∗(o).
Since ΔX is a metric space, there are many standard ways to construct topologies on MP(X). If we choose any of these standard topologies on MP(X) and then relativize it to the subspace MPbf(X), we can construct topologies on DMCX,∗(o) through the canonical bijection.
We saw in Section III-D that there are three topologies that can be constructed on MP(X): the total variation topology, the strong convergence topology, and the weak-∗ topology. But since every measure in MPbf(X) is a finitely supported measure, strong convergence and total variation convergence are equivalent in MPbf(X) (see Section III-D). Therefore, it is sufficient to study the total-variation topology and the weak-∗ topology. We will start by studying the weak-∗ topology.
X-A Weak-∗ topology
We first note that in the case of binary input channels, the weak-∗ topology is equivalent to the topology induced by the convergence in distribution of D-densities (or L-densities, or G-densities) that was defined in [3]. Note also that the weak-∗ topology is equivalent to the topology that is induced by the Le Cam deficiency distance [6].
Consider the topology on DMCX,∗(o) that is obtained by transporting the weak-∗ topology from MPbf(X) to DMCX,∗(o) through the canonical bijection Fcan, i.e., we let U⊂DMCX,∗(o) be open if and only if Fcan−1(U) is weakly-∗ open. We will call this topology the weak-∗ topology on DMCX,∗(o).
In this section, we show that the weak-∗ topology is the same as the noisiness topology TX,∗(o). We will show this using the Wasserstein metric.
Since ΔX is complete and separable, the 1st-Wasserstein distance metrizes the weak-∗ topology [14]. Therefore, in order to show that the weak-∗ topology and the noisiness topology TX,∗(o) are the same, it is sufficient to show that the canonical bijection Fcan from (MPbf(X),W1) to (DMCX,∗(o),dX,∗(o)) is a homeomorphism.
Note that since ΔX is compact, the metric space (MP(X),W1) is compact as well [14].
Lemma 7**.**
For every W^,W^′∈DMCX,∗(o), we have dX,∗(o)(W^,W^′)≤∣X∣⋅W1(MPW^,MPW^′).
Lemma 7 can also be expressed as follows: for every MP,MP′∈MPbf(X), we have dX,∗(o)(Fcan(MP),Fcan(MP′))≤∣X∣⋅W1(MP,MP′). This shows that the canonical bijection Fcan is continuous. Therefore, the weak-∗ topology is at least as strong as TX,∗(o). It remains to show that Fcan−1 is continuous. One approach to prove the continuity of Fcan−1 is to find a lower bound of dX,∗(o)(W^,W^′) in terms of the Wasserstein metric, but this is tedious. We will follow another approach in order to show that the canonical bijection Fcan is a homeomorphism. We need the following proposition:
The weak-∗ topology on DMCX,∗(o) is the same as the noisiness topology TX,∗(o).
Proof.
Let (DMCX,∗(o),dX,∗(o)) be a completion of (DMCX,∗(o),dX,∗(o)). Since MPb(X) is the weak-∗ closure of MPbf(X) (Proposition 12), we can extend the canonical bijection Fcan:MPbf(X)→DMCX,∗(o) to a mapping F:MPb(X)→DMCX,∗(o) as follows:
[TABLE]
where (MPn)n≥0 is any sequence in MPbf(X) that converges to MP∈MPb(X), and where the limit in (3) is taken inside DMCX,∗(o). In order to show that F is well defined, we have to make sure that the limit in (3) exists and that it does not depend on the sequence (MPn)n≥0.
Since the sequence (MPn)n≥0 converges, it is a Cauchy sequence. Therefore, for every ϵ>0 there exists n0>0 such that for every n1,n2≥1 we have W1(MPn1,MPn2)<∣X∣ϵ. By Lemma 7, we have
[TABLE]
Therefore, (Fcan(MPn))n≥0 is a Cauchy sequence in (DMCX,∗(o),dX,∗(o)) which is complete, hence the limit in (3) exists. Now assume that (MPn′)n≥0 is another sequence in MPbf(X) which converges to MP. We have:
[TABLE]
where (a) follows from Lemma 7 and (b) follows from the fact that (MPn)n≥0 and (MPn′)n≥0 converge to the same point. Therefore, (Fcan(MPn))n≥0 and (Fcan(MPn′))n≥0 converge to the same point in DMCX,∗(o). We conclude that F is well defined.
Now fix MP,MP′∈MPb(X) and let (MPn)n≥0 and (MPn′)n≥0 be two sequences in MPbf(X) that converge to MP and MP′ respectively. We have:
[TABLE]
where (a) and (c) follow from the fact that metric distances are continuous, and (b) follows from Lemma 7. Therefore, F is continuous from (MPb(X),W1) to (DMCX,∗(o),dX,∗(o)). Moreover, since MPb(X) is weakly-∗ closed in MP(X) which is compact, MPb(X) is compact under the weak-∗ topology. Therefore for every weakly-∗ closed subset A of MPb(X), A is compact and so F(A) is compact in (DMCX,∗(o),dX,∗(o)) which is Hausdorff. This implies that F(A) is closed in (DMCX,∗(o),dX,∗(o)) for every weakly-∗ closed subset A of MPb(X). Therefore, F is both continuous and closed. In particular, F(MPb(X)) is closed in (DMCX,∗(o),dX,∗(o)). But F(MPb(X))⊃F(MPbf(X))=Fcan(MPbf(X))=DMCX,∗(o), and DMCX,∗(o) is dense in (DMCX,∗(o),dX,∗(o)). Therefore, we must have F(MPb(X))=DMCX,∗(o). We conclude that F is a homeomorphism from (MPb(X),W1) to (DMCX,∗(o),dX,∗(o)).
Now since \overline{F}\big{(}\mathcal{MP}_{bf}(\mathcal{X})\big{)}=\operatorname*{DMC}_{\mathcal{X},\ast}^{(o)}, the restriction of F to MPbf(X) is a homeomorphism from (MPbf(X),W1) to (DMCX,∗(o),dX,∗(o)). But the restriction of F to MPbf(X) is nothing but Fcan. We conclude that the canonical bijection is a homeomorphism from (MPbf(X),W1) to (DMCX,∗(o),dX,∗(o)). Therefore, the weak-∗ topology on DMCX,∗(o) is the same as the noisiness topology TX,∗(o).
∎
Since (MPb(X),W1) is homeomorphic to (DMCX,∗(o),dX,∗(o)), we can interpret this by saying that DMCX,∗(o) is the space of all equivalent channels with input alphabet X and arbitrary output alphabet (with arbitrary cardinality). Moreover, since DMCX,∗(o) is dense in (DMCX,∗(o),dX,∗(o)), we can say that any channel with input alphabet X can be approximated in the noisiness/weak-∗ sense by a channel having a finite output alphabet.
X-B Total variation topology
The total-variation metric distancedTV,X,∗(o) on DMCX,∗(o) is defined as
[TABLE]
The total-variation topologyTTV,X,∗(o) is the metric topology that is induced by dTV,X,∗(o) on DMCX,∗(o). We will refer to the open sets (respectively, closed sets, compact sets, …) of TTV,X,∗(o) as TV-open (respectively, TV-closed, TV-compact, …). The same notation is also used for open sets of MPbf(X), MPb(X) and MP(X) in the total variation topology.
Proposition 13**.**
If ∣X∣≥2 and n≥2, then DMCX,[n](o) is not TV-compact in DMCX,∗(o).
Proof.
Let p,p′∈ΔX be such that p=p′ and 21p+21p′=πX, where πX is the uniform distribution on X. For every n≥1, define pn,pn′∈ΔX as
[TABLE]
and
[TABLE]
Clearly, 21pn+21pn′=πX for every n≥1.
Now let MPn=21δpn+21δpn′, where δpn and δpn′ are Dirac measures centered at pn and pn′ respectively. Clearly, MPn is balanced and finitely supported for every n≥1. Let W^n=Fcan(MPn). We have
[TABLE]
Therefore, W^n∈DMCX,[2](o)⊂DMCX,[m](o) for every n≥1 and every m≥2. It is easy to see that dTV,X,∗(o)(W^n1,W^n2)=∥MPn1−MPn2∥TV=1 for every n2>n1≥1. Therefore, no subsequence of (MPn)n≥1 can converge. This means that DMCX,[m](o) is not sequentially compact for any m≥2. Now since TTV,X,∗(o) is metrizable, we conclude that DMCX,[n](o) is not compact for any n≥2.
∎
Corollary 11**.**
If ∣X∣≥2, then TTV,X,∗(o) is not a natural topology.
Proof.
If TTV,X,∗(o) were natural, DMCX,[2](o) would be compact, and this is not the case.
∎
Since the noisiness topology is the same as the weak-∗ topology, TX,∗(o) is coarser than TTV,X,∗(o). On the other hand, since TX,∗(o) is natural and TTV,X,∗(o) is not, TX,∗(o) is strictly coarser than TTV,X,∗(o) when ∣X∣≥2.
Note that the sequence (MPn)n≥1 in the proof of Proposition 13 converges in the strong topology because of Proposition 9. Therefore, Ts,X,∗(o) is not finer than TTV,X,∗(o).
Although TTV,X,∗(o) is not a natural topology itself, it has many properties of natural topologies.
Proposition 14**.**
If ∣X∣≥2, every non-empty TV-open subset of DMCX,∗(o) is rank-unbounded.
Proof.
Let U be a non-empty TV-open set of DMCX,∗(o). Let W^∈U and let ϵ>0 be such that W^′∈U whenever dTV,X,∗(o)(W^,W^′)<ϵ.
Let p, p′, (pn)n≥1 and (pn′)n≥1 be as in Proposition 13. For every n≥1, define MPn∈MP(X) as follows:
[TABLE]
Clearly, MPn is balanced and finitely supported, so MPn∈MPbf(X). Moreover,
[TABLE]
Therefore, Fcan(MPn)∈U for every n≥1. On the other hand, supp(MPn)⊃{pi,pi′:1≤i≤n}, which means that ∣supp(MPn)∣≥2n and so Fcan(MPn)∈/DMCX,[n](o) for every n≥1. We conclude that U is rank-unbounded.
∎
Corollary 12**.**
If ∣X∣≥2, the TV-interior of DMCX,[n](o) in DMCX,∗(o) is empty.
Note that the sequence (Fcan(MPn))n≥1 in the proof of Proposition 14 is rank-unbounded and converges in total variation to W^. On the other hand, Proposition 9 implies that (Fcan(MPn))n≥1 does not converge in (DMCX,∗(o),Ts,X,∗(o)). We conclude that TTV,X,∗(o) is not finer than Ts,X,∗(o).
Although DMCX,[n](o) is not TV-compact if ∣X∣≥2 and n≥2, it is TV-complete:
Proposition 15**.**
For every n≥1, DMCX,[n](o) is TV-complete in DMCX,∗(o).
Proof.
Let MPb,n(X) be the set of balanced meta-probability measures whose support is of size at most n:
[TABLE]
Since (DMCX,[n](o),dTV,X,∗(o)) is isometric to (MPb,n(X),∥⋅∥TV), and since (MP(X),∥⋅∥TV) is complete, it is sufficient to show that MPb,n(X) is TV-closed in MP(X).
Let MP be in the TV-closure of MPb,n(X). Since we are working in a metric space, there exists a sequence (MPm)m≥0 in MPb,n(X) that TV-converges to MP. Assume that MP∈/MPb,n(X). There exist p1,…,pn+1∈ΔX that are pairwise different and which satisfy MP(pi)>0 for every 1≤i≤n+1. Since (MPm)m≥0 TV-converges to MP, there exists m0≥0 such that MPm0(pi)>0 for every 1≤i≤n+1. This contradicts the fact MPm0∈MPb,n(X). Therefore, MP∈MPb,n(X) for every MP in the TV-closure of MPb,n(X). This shows that MPb,n(X) is TV-closed. Therefore, DMCX,[n](o) is TV-complete in DMCX,∗(o).
∎
Proposition 16**.**
If ∣X∣≥2, (DMCX,∗(o),TTV,X,∗(o)) is neither Baire nor locally compact anywhere.
Proof.
Since DMCX,[n](o) is TV-complete, it is TV-closed. Since it also has empty TV-interior, the same techniques that were used for natural topologies in Section VII-A can be applied for TTV,X,∗(o).
∎
The above proposition shows that (DMCX,∗(o),TTV,X,∗(o)) cannot be completely metrized. Note that since (DMCX,∗(o),dTV,X,∗(o)) is isometric to (MPbf(X),∥⋅∥TV), and since (MP(X),∥⋅∥TV) is complete, the completion of (DMCX,∗(o),dTV,X,∗(o)) is isometric to the closure of MPbf(X) in (MP(X),∥⋅∥TV). It can be shown that the TV-closure of MPbf(X) in MP(X) is the set of all balanced and countably supported meta-probability measures on X. Therefore, the completion of (DMCX,∗(o),dTV,X,∗(o)) can be thought of as the space of equivalent channels from X to a countably infinite output alphabet. This allows us to say that any channel with input alphabet X and a countable output alphabet can be approximated in the total variation sense by a channel having a finite output alphabet.
XI The natural Borel σ-algebra on DMCX,∗(o)
Let T be a Hausdorff natural topology on DMCX,∗(o). Since Ts,X,∗(o) is the finest natural topology, we have T⊂Ts,X,∗(o). Therefore, B(T)⊂B(Ts,X,∗(o)).
On the other hand, for every U∈Ts,X,∗(o) and every n≥1, we have U∩DMCX,[n](o)∈TX,[n](o). But T is a natural topology, so there must exist Un∈T such that Un∩DMCX,[n](o)=U∩DMCX,[n](o). Since Un∈T, we have Un∈B(T). Moreover, DMCX,[n](o) is T-closed (because it is compact and T is Hausdorff). Therefore, DMCX,[n](o)∈B(T). This implies that U∩DMCX,[n](o)=Un∩DMCX,[n](o)∈B(T), hence
[TABLE]
Since this is true for every U∈Ts,X,∗(o), we have Ts,X,∗(o)⊂B(T) which implies that B(Ts,X,∗(o))⊂B(T). We conclude that all Hausdorff natural topologies on DMCX,∗(o) have the same σ-algebra. This σ-algebra deserves to be called the natural Borel σ-algebra on DMCX,∗(o).
Note that for every n≥1, the inclusion mapping in:DMCX,[n](o)→DMCX,∗(o) is continuous from (DMCX,[n](o),TX,[n](o)) to (DMCX,∗(o),Ts,X,∗(o)), hence it is measurable. Therefore, for every B∈B(Ts,X,∗(o)), we have in−1(B)=B∩DMCX,[n](o)∈B(TX,[n](o)). In the following, we show a converse for this statement.
Fix n≥1 and let U∈TX,[n](o). There exists U′∈Ts,X,∗(o) such that U=U′∩DMCX,[n](o). Since U′ and DMCX,[n](o) are respectively open and closed in the topology Ts,X,∗(o), they are both in its Borel σ-algebra. Therefore, U=U′∩DMCX,[n](o)∈B(Ts,X,∗(o)) for every U∈TX,[n](o). This means that TX,[n](o)⊂B(Ts,X,∗(o)) and B(TX,[n](o))⊂B(Ts,X,∗(o)) for every n≥1.
Assume now that A⊂DMCX,∗(o) satisfies A∩DMCX,[n](o)∈B(TX,[n](o)) for every n≥1. This implies that A∩DMCX,[n](o)∈B(Ts,X,∗(o)) for every n≥1, hence
[TABLE]
We conclude that a subset A of DMCX,∗(o) is in the natural Borel σ-algebra if and only if A∩DMCX,[n](o)∈B(TX,[n](o)) for every n≥1.
XII Conclusion
The fact that the noisiness and weak-∗ topologies are the same gives us more freedom in proving theorems. Statements that can be hard to prove using the weak-∗ formulation might be easier to prove using the noisiness formulation. For example, the convergence of the polarization process is slightly easier to prove in the noisiness formulation [9].
The strong topology is too strong to be adopted as the “standard natural topology”. However, it can still be useful because it is relatively easy to work with as it has a quotient formulation. Moreover, since it is finer than the noisiness/weak-∗ topology, many statements that are true for the strong topology are also true for coarser topologies, e.g., any sequence that converges in the strong topology also converges in the noisiness/weak-∗ topology.
Although the total variation topology is not natural, it can still be useful because it is finer than the noisiness/weak-∗ topology.
Many interesting questions remain open: Are all natural topologies Hausdorff? Can we find more topological properties that are common for all natural topologies? Is there a coarsest natural topology? Is there a natural topology that is coarser than the noisiness/weak-∗ one?
Finding meaningful measures on DMCX,∗(o) might be challenging. One might be tempted to require that the measure of DMCX,[n](o) should be zero because it is “finite dimensional” whereas DMCX,∗(o) is “infinite dimensional”. On the other hand, if DMCX,[n](o) has a zero measure for every n≥1, the whole space DMCX,∗(o) will have a zero measure because it is a countable union of these subspaces. Nevertheless, statements such as “the property X is true for almost all channels” can still make sense. One possible definition of null-sets is as follows: for every set A in the natural Borel σ-algebra, we say that A is a null-set if and only if there exists n0≥1 such that Pn(Projn−1(A∩DMCX,[n](o)))=0 for every n≥n0, where Projn is the projection onto the RX,[n](o)-equivalence classes and Pn is the uniform probability measure on DMCX,[n]≡(Δ[n])X. Another possible definition, which is weaker, is to say that A is a null-set if and only if n→∞limPn(Projn−1(A∩DMCX,[n](o)))=0.
It is worth mentioning that the standard weak-∗ (and in particular the L-density) approach is not possible in the quantum setting because there is no quantum analogue for the conditional probability of the input given the output. On the other hand, since the strong topology and the noisiness metric are defined in terms of the forward transition probabilities, they can be generalized to the quantum setting.
Another notion of equivalence is the Shannon-equivalence that allows randomization at both the input and the output, as well as shared randomness between the transmitter and the receiver [17]. The Shannon deficiency that was introduced in [18] compares a particular channel with the Shannon-equivalence-class of another channel, but it is not a metric distance between Shannon-equivalence-classes. In [19], we provide a characterization of the Shannon ordering and we prove that some of the results of this paper holds for the space of Shannon-equivalent channels.
In [20], we introduce the notions of input-degradedness and input-equivalence. A channel W is said to be input-degraded from another channel W′ if W can be simulated from W′ by local operations at the input. In [20], we provide a characterization of input-degradedness and and we prove that many of the results of this paper hold for the space of input-equivalent channels.
Acknowledgment
I would like to thank Emre Telatar and Mohammad Bazzi for helpful discussions. I am also grateful to Maxim Raginsky for his comments.
For every A⊂ΔX, let co(A) be the convex hull of A. We say that p∈A is convex-extreme if it is an extreme point of co(A), i.e., for every p1,…,pn∈co(A) and every λ1,…,λn>0 satisfying i=1∑nλi=1 and i=1∑nλipi=p, we have p1=…=pn=p. It is easy to see that if A is finite, then the convex-extreme points of A coincide with the extreme points of co(A). We denote the set of convex-extreme points of A as CE(A).
Let W∈DMCX,Y and W′∈DMCX,Z be such that W′ is degraded from W. There exists V∈DMCY,Z such that W′=V∘W. Let X be a random variable uniformly distributed in X, let Y be the output of W when X is the input, and let Z be the output of V when Y is the input in such a way that X−Y−Z is a Markov chain. Clearly, PZ∣X(z∣x)=W′(z∣x) for every (x,z)∈X×Z.
For every z∈Z, we have:
[TABLE]
Define V−1∈DMCIm(W′),Im(W) as
[TABLE]
Note that for every (y,z)∈Im(W)×Im(W′), we have V−1(y∣z)=0 if and only if V(z∣y)=0.
For every (x,z)∈X×Im(W′), we have:
[TABLE]
where (a) follows from the fact that X−Y−Z is a Markov chain.
Equation (5) shows that for every z∈Im(W′), we have
[TABLE]
Therefore,
[TABLE]
Now for every p∈ΔX, define
[TABLE]
Similarly,
[TABLE]
Let pext∈CE(supp(MPW)) and let z∈Im(W′). Equation (5) shows that if z∈Zpext, then V−1(y∣z)=0 for every y∈Im(W)∖Ypext. Now since V−1(y∣z)=0⇔V(z∣y)=0 for every (y,z)∈Im(W)×Im(W′), we deduce that if z∈Zpext then V(z∣y)=0 for every y∈Im(W)∖Ypext. Therefore,
[TABLE]
where (a) follows from Equation (4), and (b) follows from the fact that for every y∈Im(W)∖Ypext, we have V(z∣y)=0.
Now assume that W and W′ are equivalent. Equation (6) (applied twice) implies that we must have co(supp(MPW′))=co(supp(MPW)) which implies that supp(MPW′) and supp(MPW) have the same convex-extreme points. Now fix a convex-extreme point pext∈CE(supp(MPW′))=CE(supp(MPW)). Equation (7) (applied twice) implies that MPW(pext)=MPW′(pext). By using Equation (7) again we obtain:
[TABLE]
hence
[TABLE]
But PWo(y)>0 for every y∈Ypext. Therefore, for every z∈Im(W′)∖Zpext and every y∈Ypext, we must have V(z∣y)=0 (which implies that V−1(y∣z)=0). We conclude that for every z∈Im(W′)∖Zpext, we can rewrite Equations (4) and (5) as:
[TABLE]
and
[TABLE]
We can now repeat the above argument but on supp(MPW)∖{pext} and supp(MPW′)∖{pext} instead of supp(MPW) and supp(MPW′). We deduce that co(supp(MPW)∖{pext})=co(supp(MPW′)∖{pext}) so supp(MPW)∖{pext} and supp(MPW′)∖{pext} have the same convex-extreme points. We can also prove that MPW(pext′)=MPW′(pext′) for every pext′∈CE(supp(MPW′)∖{pext})=CE(supp(MPW)∖{pext}).
Notice that any point of supp(MPW) (respectively supp(MPW′)) becomes convex-extreme after removing a finite number of elements from supp(MPW) (respectively supp(MPW′)). Therefore, after inductively applying the above argument a finite number of times, we can deduce that supp(MPW)=supp(MPW′) and MPW(p)=MPW′(p) for every p∈supp(MPW)=supp(MPW′), hence MPW=MPW′.
Now let W∈DMCX,Y and W′∈DMCX,Z be any two channels satisfying MPW=MPW′. We have supp(MPW)=supp(MPW′), and for every p∈supp(MPW)=supp(MPW′), we have
[TABLE]
Define the channel V∈DMCY,Z as
[TABLE]
A simple calculation shows that z∈Z∑V(z∣y)=1 for every y∈Y, so V is a valid channel.
Notice that for every (y,z)∈Im(W)×Im(W′), we have:
[TABLE]
Moreover, if z∈Im(W′)andy∈YWz′−1, we have MPW′(Wy−1)=MPW(Wz′−1). Therefore, we can rewrite V as:
[TABLE]
Let W′′=V∘W∈DMCX,Z. For every z∈Z∖Im(W′), Equation (4) implies that:
[TABLE]
where (a) follows from the fact that V(z∣y)=0 if y∈Im(W) and z∈/Im(W′).
On the other hand, for every z∈Im(W′), Equation (4) implies that:
[TABLE]
Therefore, PW′′o(z)=PW′o(z) for every z∈Z, which implies that Im(W′′)=Im(W′).
Now define V−1∈DMCIm(W′′),Im(W) as
[TABLE]
Equation (5) implies that for every z∈Im(W′′)=Im(W′), we have:
[TABLE]
where (a) and (b) follow from the fact that for every (y,z)∈Im(W)×Im(W′′), we have V−1(y∣z)=0 if and only if V(z∣y)=0.
We conclude that PW′′o=PW′o, and for every z∈Im(W′′)=Im(W′), we have Wz′′−1=Wz′−1. Therefore, W′=W′′=V∘W and so W′ is degraded from W. By exchanging the roles of W and W′ we get that W is also degraded from W′, hence W and W′ are equivalent.
The relation RX,Y(o) is closed in DMCX,Y×DMCX,Y.
Proof.
Define the mapping f:(DMCX,Y)2×(DMCY,Y)2→(DMCX,Y)4 as:
[TABLE]
f is continuous because channel composition is continuous.
Define the set A⊂(DMCX,Y)4 as:
[TABLE]
It is easy to see that A is a closed subset of (DMCX,Y)4. We have:
[TABLE]
Since f is continuous and since A is a closed subset of (DMCX,Y)4, f−1(A) is a closed subset of (DMCX,Y)2×(DMCY,Y)2 which is compact. Therefore, f−1(A) is compact.
Now define the mapping g:(DMCX,Y)2×(DMCY,Y)2→(DMCX,Y)2 as follows:
[TABLE]
Since g is continuous and since f−1(A) is compact, g(f−1(A)) is a compact subset of DMCX,Y2. Now notice that
[TABLE]
We conclude that RX,Y(o) is compact, hence it is also closed because (DMCX,Y)2 is a metric space.
∎
Let Proj:DMCX,Y→DMCX,Y(o) be defined as Proj(W)=W^. The continuity of Proj follows from the definition of the quotient topology.
Now let A be a closed subset of DMCX,Y. We want to show that Proj(A) is closed.
Since A is closed in DMCX,Y, the set DMCX,Y×A is closed in (DMCX,Y)2. On the other hand, RX,Y(o) is closed in (DMCX,Y)2 by Lemma 8. Therefore, (DMCX,Y×A)∩RX,Y(o) is closed in (DMCX,Y)2 which is compact, hence (DMCX,Y×A)∩RX,Y(o) is compact. We have:
[TABLE]
Now define the mapping g:(DMCX,Y)2→DMCX,Y as
[TABLE]
Let A_{R}:=g\big{(}(\operatorname*{DMC}_{\mathcal{X},\mathcal{Y}}\times A)\cap R_{\mathcal{X},\mathcal{Y}}^{(o)}\big{)}. Since g is continuous and since (DMCX,Y×A)∩RX,Y(o) is compact, AR is also compact. We have:
[TABLE]
Since DMCX,Y is a metric space and since AR is compact, Proj−1(Proj(A))=AR is closed in DMCX,Y. On the other hand, we have \operatorname*{Proj}^{-1}\big{(}\operatorname*{DMC}_{\mathcal{X},\mathcal{Y}}^{(o)}\setminus\operatorname*{Proj}(A)\big{)}=\operatorname*{DMC}_{\mathcal{X},\mathcal{Y}}\setminus\operatorname*{Proj}^{-1}(\operatorname*{Proj}(A)), hence \operatorname*{Proj}^{-1}\big{(}\operatorname*{DMC}_{\mathcal{X},\mathcal{Y}}^{(o)}\setminus\operatorname*{Proj}(A)\big{)} is open in DMCX,Y, which implies that DMCX,Y(o)∖Proj(A) is open in DMCX,Y(o). Therefore, Proj(A) is closed in DMCX,Y(o).
let U^ be an arbitrary non-empty open subset of (DMCX,[m](o),TX,[m](o)) and let Proj be the projection onto the RX,[m](o)-equivalence classes. Proj−1(U^) is open in the metric space (DMCX,[m],dX,[m]). Therefore, there exists W∈DMCX,[m] and ϵ>0 such that Proj−1(U^) contains the open ball of center W and radius ϵ.
We will show that there exists W′∈DMCX,[m] such that rank(W′)=m>n and dX,[m](W,W′)<ϵ. If rank(W)=m, take W′=W.
Assume that rank(W)<m. Let PWo∈Δ[m], Im(W) and {Wy−1:y∈Im(W)} be as in Section V.
Let {vy}y∈[m] be a collection of m vectors in RX such that:
•
y∈Im(W)∑PWo(y)⋅vy=0.
•
y∈[m]∖Im(W)∑vy=0.
•
For every y∈[m], x∈X∑vy(x)=0.
•
The vectors {vy}y∈[m] are pairwise different.
Such collection can always be found.
Let 0<δ,δ′<1 and define PW′o∈R[m] as follows:
[TABLE]
Clearly, PW′o∈Δ[m] and PW′o(y)>0 for every y∈[m]. Now for every y∈[m], define Wy′−1 as follows:
[TABLE]
where πX∈ΔX is the uniform probability distribution on X. A simple calculation shows that y∈[m]∑PW′o(y)Wy′−1=πX, and for every y∈[m] we have x∈X∑Wy′−1(x)=1.
Notice that for y∈Im(W), since 0<δ<1, (1−δ)Wy−1+δπX lies inside the interior of the probability distribution simplex ΔX. This means that for δ′ small enough, (1−δ)Wy−1+δπX+δ′vy∈ΔX for every y∈Im(W), and πX+δ′vy∈ΔX for every y∈/Im(W). For every 0<δ<1, choose δ′:=δ′(δ) so that 0<δ′<δ and Wy′−1∈ΔX for every y∈[m].
It is easy to see that for δ small enough, Wy1′−1=Wy2′−1 for every y1,y2∈[m] satisfying y1=y2. Define the channel W′∈DMCX,[m] as follows:
[TABLE]
Since PW′o(y)>0 for every y∈[m], we have supp(MPW′)={Wy′−1:y∈[m]}. Therefore, there exists δ0>0 such for every 0<δ<δ0, we have rank(W′)=m. On the other hand, we have δ→0limPW′o=PWo and δ→0limWy′−1=Wy−1 for every y∈Im(W). Therefore, δ→0limW′=W (where the limit is taken in (DMCX,[m],dX,[m])). This shows that there exists W′∈DMCX,[m] such that rank(W′)=m>n and dX,[m](W,W′)<ϵ, which means that W′∈Proj−1(U^) and W′ is not equivalent to any channel in DMCX,[n] (see Corollary 1). Therefore, Proj(W′)∈U^ and Proj(W′)∈/DMCX,[n](o) because W′ is not equivalent to any channel in DMCX,[n]. This shows that every non-empty open subset of DMCX,[m](o) is not contained in DMCX,[n](o). We conclude that the interior of DMCX,[n](o) in DMCX,[m](o) is empty.
Define DMCX,[0](o)=\o, which is strongly closed in DMCX,∗(o).
Let A and B be two disjoint strongly closed subsets of DMCX,∗(o). For every n≥0, let An=A∩DMCX,[n](o) and Bn=B∩DMCX,[n](o). Since A and B are strongly closed in DMCX,∗(o), An and Bn are closed in DMCX,[n](o). Moreover, An∩Bn⊂A∩B=\o.
Construct the sequences (Un)n≥0,(Un′)n≥0,(Kn)n≥0 and (Kn′)n≥0 recursively as follows:
U0=U0′=K0=K0′=\o⊂DMCX,[0](o). Since A0=B0=\o, we have A0⊂U0⊂K0 and B0⊂U0′⊂K0′. Moreover, U0 and U0′ are open in DMCX,[0](o), K0 and K0′ are closed in DMCX,[0](o), and K0∩K0′=\o.
Now let n≥1 and assume that we constructed (Ui)0≤i<n,(Ui′)0≤i<n,(Ki)0≤i<n and (Ki′)0≤i<n such that for every 0≤i<n, we have Ai⊂Ui⊂Ki⊂DMCX,[i](o), Bi⊂Ui′⊂Ki′⊂DMCX,[i](o), Ui and Ui′ are open in DMCX,[i](o), Ki and Ki′ are closed in DMCX,[i](o), and Ki∩Ki′=\o. Moreover, assume that Ki⊂Ui+1 and Ki′⊂Ui+1′ for every 0≤i<n−1.
Let Cn=An∪Kn−1 and Dn=Bn∪Kn−1′. Since Kn−1 and Kn−1′ are closed in DMCX,[n−1](o) and since DMCX,[n−1](o) is closed in DMCX,[n](o), we can see that Kn−1 and Kn−1′ are closed in DMCX,[n](o). Therefore, Cn and Dn are closed in DMCX,[n](o). Moreover, we have
[TABLE]
where (a) follows from the fact that An∩Bn=Kn−1∩Kn−1′=\o and the fact that Kn−1⊂DMCX,[n−1](o) and Kn−1′⊂DMCX,[n−1](o).
Since DMCX,[n](o) is normal (because it is metrizable), and since Cn and Dn are closed disjoint subsets of DMCX,[n](o), there exist two sets Un,Un′⊂DMCX,[n](o) that are open in DMCX,[n](o) and two sets Kn,Kn′⊂DMCX,[n](o) that are closed in DMCX,[n](o) such that Cn⊂Un⊂Kn, Dn⊂Un′⊂Kn′ and Kn∩Kn′=\o. Clearly, An⊂Un⊂Kn⊂DMCX,[n](o), Bn⊂Un′⊂Kn′⊂DMCX,[n](o), Kn−1⊂Un and Kn−1′⊂Un′. This concludes the recursive construction.
Now define U=n≥0⋃Un=n≥1⋃Un and U′=n≥0⋃Un′=n≥1⋃Un′. Since An⊂Un for every n≥1, we have
[TABLE]
Moreover, for every n≥1 we have
[TABLE]
where (a) follows from the fact that Ui⊂Ki⊂Ui+1 for every i≥0, which means that the sequence (Ui)i≥1 is increasing.
For every i≥n, we have DMCX,[n](o)⊂DMCX,[i](o) and Ui is open in DMCX,[i](o), hence Ui∩DMCX,[n](o) is open in DMCX,[n](o). Therefore, U∩DMCX,[n](o)=i≥n⋃(Ui∩DMCX,[n](o)) is open in DMCX,[n](o). Since this is true for every n≥1, we conclude that U is strongly open in DMCX,∗(o).
We can similarly show that B⊂U′ and that U′ is strongly open in DMCX,∗(o). Finally, we have
[TABLE]
where (a) follows from the fact that for every n≥1 and every n′≥1, we have
[TABLE]
because (Un)n≥1 and (Un′)n≥1 are increasing. We conclude that (DMCX,∗(o),Ts,X,∗(o)) is normal.
Let γ∈Γ(MPW^,MPW^′) be a measure on ΔX×ΔX that couples MPW^ and MPW^′.
Let S=supp(MPW^) and S′=supp(MPW^′) be the supports of W^ and W^′ respectively. Since MPW^ and MPW^′ are finitely supported, γ is also finitely supported and its support is a subset of S×S′. Therefore, there exists a collection of coefficients αp,p′∈[0,1] such that
[TABLE]
where δ(p,p′) is a Dirac measure centered at (p,p′)∈ΔX×ΔX. Since MPW^ and MPW^′ are the marginals of γ on the first and the second factors respectively, we have MPW^(p)=p′∈S′∑αp,p′ for every p∈S. Similarly, MPW^′(p′)=p∈S∑αp,p′ for every p′∈S′.
Let Y=S×S′ and define the channels W,W′∈DMCX,Y as:
[TABLE]
and
[TABLE]
For every x∈X, we have
[TABLE]
Similarly, (p,p′)∈Y∑W′(p,p′∣x)=1. Therefore, W and W′ are valid channels.
For every (p,p′)∈Y, we have
[TABLE]
Therefore, Im(W)={(p,p′)∈Y:αp,p′>0}. For every (p,p′)∈Im(W) and every x∈X, we have:
[TABLE]
hence Wp,p′−1=p for every (p,p′)∈Im(W), which shows that supp(MPW)⊂S. Similarly, we can show that
[TABLE]
supp(MPW′)⊂S′, and for every (p,p′)∈Y, PW′o(p,p′)=αp,p′ and Wp,p′′−1=p′.
For every p∈S, we have:
[TABLE]
This shows that supp(MPW)=S=supp(MPW^) and MPW(p)=MPW^(p) for every p∈S. Therefore, MPW=MPW^ and so W is equivalent to every channel in W^. Similarly, we can show that MPW′=MPW^′ and W′ is equivalent to every channel in W^′.
Let W~ and W~′ be the RX,Y(o)-equivalence classes of W and W′ respectively. We can write W^=W~ and W^′=W~′ because of the canonical identification of DMCX,Y(o) with DMCX,[n](o), where n=∣Y∣. We have:
[TABLE]
where (a) follows from Lemma 6, and d(p,p′)=21∥p−p′∥1 is the total variation distance between p and p′. Therefore,
If ∣X∣=1, ΔX consists of a single probability distribution and MP(X) consists of a single meta-probability measure which is balanced and finitely supported, so MP(X)=MPb(X)=MPbf(X).
Now assume that ∣X∣≥2. We start by showing that MPb(X) is weakly-∗ closed.
For every x∈X. Consider the mapping fx:ΔX→R defined as fx(p)=p(x). Clearly, fx is bounded and continuous. Therefore, the mapping
[TABLE]
defined as
[TABLE]
is continuous in the weak-∗ topology. Therefore, Fx−1({∣X∣1}) is weakly-∗ closed. It is easy to see that MPb(X)=x∈X⋂Fx−1({∣X∣1}). This proves that MPb(X), which is the finite intersection of weakly-∗ closed sets, is weakly-∗ closed.
It remains to show that MPbf(X) is weakly-∗ dense in MPb(X). We will show that for every ϵ>0 and every MP∈MPb(X), there exists MP′∈MPbf(X) such that W1(MP,MP′)<ϵ.
Fix 0<ϵ<1 and let MP∈MPb(X) be any balanced meta-probability measure on X, i.e., for every x∈X we have
[TABLE]
Now fix x∈X. By the definition of the Lebesgue integral, there exists a finite partition {Bx,i}1≤i≤kx of ΔX and a sequence of positive numbers (bx,i)1≤i≤kx such that for every 1≤i≤kx, Bx,i is a Borel set of ΔX, bx,i≤p(x) for every p∈Bx,i, and
[TABLE]
By applying the same reasoning on the function 1−p(x)≥0, we can find a finite partition {Cx,i}1≤i≤mx of ΔX and a sequence of positive numbers (cx,i)1≤i≤mx such that for every 1≤i≤mx, Cx,i is a Borel set of ΔX, cx,i≥p(x) for every p∈Cx,i and
[TABLE]
Let d be the total variation distance on ΔX, i.e., d(p,p′)=21∥p−p′∥1. Since ΔX is compact, it can be covered by a finite number of open balls of radius 4ϵ, i.e., there exist h points p1′,…,ph′ such that ΔX=i=1⋃hB4ϵ(pi′)=i=1⋃h{p∈ΔX:d(p,pi′)<4ϵ}. For every 1≤i≤h, define the set
[TABLE]
Clearly, the sets {Di}1≤i≤h are disjoint Borel sets that cover ΔX. Let n=h×x∈X∏(kx⋅mx), and let A1,…,An be the Borel sets obtained by intersecting the sets in the collections {D1,…,Dh}, {Bx,i}1≤i≤kx and {Cx,i}1≤i≤mx for every x∈X. In other words,
[TABLE]
For every 1≤i≤n, let lx,i=bx,i′ where i′ is the unique integer satisfying 1≤i′≤kx and Ai⊂Bx,i′. Similarly, let ux,i=cx,i′′ where i′′ is the unique integer satisfying 1≤i′′≤kx and Ai⊂Cx,i′′. Clearly, lx,i≤p(x)≤ux,i for every x∈Ai. Moreover,
[TABLE]
and
[TABLE]
For every 1≤i≤n, choose pi∈Ai arbitrarily. Let ji be the unique integer such that Ai⊂Dji. Since Dji⊂B4ϵ(pji′), we have d(p,pji′)<4ϵ for every p∈Ai. Therefore, d(p,pi)≤d(p,pji′)+d(pji′,pi)<2ϵ for every p∈Ai.
Define the mapping f:ΔX→ΔX as f(p)=pi for every p∈Ai. Clearly, d(p,f(p))<2ϵ for every p∈ΔX.
Now let MPf=f#(MP), where f#(MP) is the push-forward measure of MP by the mapping f, i.e., {\operatorname*{MP}}_{f}(B)=(f_{\#}({\operatorname*{MP}}))(B)={\operatorname*{MP}}\big{(}f^{-1}(B)\big{)} for every Borel set B of ΔX. We have:
[TABLE]
where αi=MP(Ai) for every 1≤i≤n. Therefore, MPf is finitely supported and
[TABLE]
Moreover, MPf(pi)=αi for every 1≤i≤n.
Now define the mapping f×:ΔX→ΔX×ΔX as f×(p)=(p,f(p)), and define the measure γf on ΔX×ΔX as the push-forward of MP by f×, i.e., γf(B)=MP(f×−1(B)) for every Borel set B of ΔX×ΔX. It is easy to see that the marginals of γf on the first and second factors are MP and MPf respectively. Therefore, γf is a coupling between MP and MPf, hence
[TABLE]
where (a) follows from the fact that γf is the push-forward of MP by f×(p)=(p,f(p)). (b) follows from the fact that d(p,f(p))<2ϵ for every p∈ΔX. Therefore, MPf well approximates MP and it is finitely supported. However, MPf may not be balanced, so more work needs to be done in order to find a balanced and finitely supported meta-probability measure that well approximates MP.
For every x∈X, we have:
[TABLE]
where (a) follows from the fact that MPf is the push-forward of MP by f. (b) follows from the fact that pi∈Ai and so pi(x)≥li,x for every 1≤i≤n. Similarly, we have
[TABLE]
where (c) follows from the fact that pi∈Ai and so pi(x)≤ui,x for every 1≤i≤n. We conclude that for every x∈X, we have
[TABLE]
where πX is the uniform distribution on X. Define p~∈ΔX as:
[TABLE]
For every x∈X, define
[TABLE]
Clearly, x∈X∑p′(x)=1. Moreover,
[TABLE]
Where (a) follows from the fact that ∣πX(x)−p~(x)∣≤12∣X∣ϵ. We conclude that p′∈ΔX. Now define the meta-probability measure MP′ as follows:
[TABLE]
where δp′ is a Dirac measure centered at p′.
For every x∈X, we have
[TABLE]
Therefore, MP′ is balanced and finitely supported. Moreover,
[TABLE]
where (a) follows from the fact that the 1st Wasserstein metric is upper bounded by the total variation multiplied by the diameter of ΔX (which is equal to 1 in our case) [14]. We conclude that MPbf(X) is dense in MPb(X) which is weakly-∗ closed. Therefore, MPb(X) is the weak-∗ closure of MPbf(X).
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