On Linear Operator Channels over Finite Fields

TL;DR

This paper investigates the capacity of linear operator channels over finite fields, focusing on subspace coding, channel training, and proposing coding schemes that approach optimal rates without full channel knowledge.

Contribution

It provides bounds on the achievable rates of subspace coding, characterizes the rate for constant-dimensional codes, and introduces two channel training-based coding schemes.

Findings

Lower bound on subspace coding rate is tight in some cases.

Constant-dimensional subspace coding incurs negligible rate loss.

Proposed coding schemes perform close to theoretical bounds.

Abstract

Motivated by linear network coding, communication channels perform linear operation over finite fields, namely linear operator channels (LOCs), are studied in this paper. For such a channel, its output vector is a linear transform of its input vector, and the transformation matrix is randomly and independently generated. The transformation matrix is assumed to remain constant for every T input vectors and to be unknown to both the transmitter and the receiver. There are NO constraints on the distribution of the transformation matrix and the field size. Specifically, the optimality of subspace coding over LOCs is investigated. A lower bound on the maximum achievable rate of subspace coding is obtained and it is shown to be tight for some cases. The maximum achievable rate of constant-dimensional subspace coding is characterized and the loss of rate incurred by using constant-dimensional subspace coding is insignificant. The maximum achievable rate of channel training is close to the lower bound on the maximum achievable rate of subspace coding. Two coding approaches based on channel training are proposed and their performances are evaluated. Our first approach makes use of rank-metric codes and its optimality depends on the existence of maximum rank distance codes. Our second approach applies linear coding and it can achieve the maximum achievable rate of channel training. Our code designs require only the knowledge of the expectation of the rank of the transformation matrix. The second scheme can also be realized ratelessly without a priori knowledge of the channel statistics.