On the Capacity of Multiplicative Finite-Field Matrix Channels

TL;DR

This paper analyzes the capacity of a finite-field matrix channel with a model based on rank distribution, providing a convex optimization formulation and insights into asymptotic behavior and input strategies.

Contribution

It introduces a flexible, tractable model for multiplicative finite-field matrix channels and derives a convex optimization approach for capacity calculation.

Findings

Channel capacity expressed as a convex optimization problem

Closed-form capacity for constant-rank input case

Constant-rank input suffices asymptotically

Abstract

This paper deals with the multiplicative finite-field matrix channel, a discrete memoryless channel whose input and output are matrices (over a finite field) related by a multiplicative transfer matrix. The model considered here assumes that all transfer matrices with the same rank are equiprobable, so that the channel is completely characterized by the rank distribution of the transfer matrix. This model is seen to be more flexible than previously proposed ones in describing random linear network coding systems subject to link erasures, while still being sufficiently simple to allow tractability. The model is also conservative in the sense that its capacity provides a lower bound on the capacity of any channel with the same rank distribution. A main contribution is to express the channel capacity as the solution of a convex optimization problem which can be easily solved by numerical computation. For the special case of constant-rank input, a closed-form expression for the capacity is obtained. The behavior of the channel for asymptotically large field size or packet length is studied, and it is shown that constant-rank input suffices in this case. Finally, it is proved that the well-known approach of treating inputs and outputs as subspaces is information-lossless even in this more general model.