On Decoding of DVR-Based Linear Network Codes

TL;DR

This paper analyzes decoding delays in DVR-based linear network codes, proving the optimality of time-invariant decoding delay and introducing a more flexible time-variant decoding approach applicable to convolutional network coding.

Contribution

It establishes the optimality of the existing time-invariant decoding delay and introduces a new time-variant decoding method with enhanced flexibility for DVR-based LNC.

Findings

Time-invariant decoding delay is proven to be optimal.

Time-variant decoding offers greater flexibility in decoding.

Results apply specifically to convolutional network coding.

Abstract

The conventional theory of linear network coding (LNC) is only over acyclic networks. Convolutional network coding (CNC) applies to all networks. It is also a form of LNC, but the linearity is w.r.t. the ring of rational power series rather than the field of data symbols. CNC has been generalized to LNC w.r.t. any discrete valuation ring (DVR) in order for flexibility in applications. For a causal DVR-based code, all possible source-generated messages form a free module, while incoming coding vectors to a receiver span the \emph{received submodule}. An existing \emph{time-invariant decoding} algorithm is at a delay equal to the largest valuation among all invariant factors of the received submodule. This intrinsic algebraic attribute is herein proved to be the optimal decoding delay. Meanwhile, \emph{time-variant decoding} is formulated. The meaning of time-invariant decoding delay gets a new interpretation through being a special case of the time-variant counterpart. The optimal delay turns out to be the same for time-variant decoding, but the decoding algorithm is more flexible in terms of decodability check and decoding matrix design. All results apply, in particular, to CNC.