Table of Contents
Fetching ...

Information Rates of Channels with Additive White Cauchy Noise

Shuqin Pang, Wenyi Zhang

TL;DR

It is shown that the AWCN decoder can be applied to additive white Gaussian noise (AWGN) channels with negligible rate loss, while the AWGN decoder when applied to AWCN channels cannot ensure reliable decoding.

Abstract

Information transmission over discrete-time channels with memoryless additive noise obeying a Cauchy, rather than Gaussian, distribution, are studied. The channel input satisfies an average power constraint. Upper and lower bounds to such additive white Cauchy noise (AWCN) channel capacity are established. In the high input power regime, the gap between upper and lower bounds is within 0.5 nats per channel use, and the lower bound can be achieved with Gaussian input. In the lower input power regime, the capacity can be asymptotically approached by employing antipodal input. It is shown that the AWCN decoder can be applied to additive white Gaussian noise (AWGN) channels with negligible rate loss, while the AWGN decoder when applied to AWCN channels cannot ensure reliable decoding. For the vector receiver case, it is shown that a linear combining receiver front end loses the channel combining gain, a phenomenon drastically different from AWGN vector channels.

Information Rates of Channels with Additive White Cauchy Noise

TL;DR

It is shown that the AWCN decoder can be applied to additive white Gaussian noise (AWGN) channels with negligible rate loss, while the AWGN decoder when applied to AWCN channels cannot ensure reliable decoding.

Abstract

Information transmission over discrete-time channels with memoryless additive noise obeying a Cauchy, rather than Gaussian, distribution, are studied. The channel input satisfies an average power constraint. Upper and lower bounds to such additive white Cauchy noise (AWCN) channel capacity are established. In the high input power regime, the gap between upper and lower bounds is within 0.5 nats per channel use, and the lower bound can be achieved with Gaussian input. In the lower input power regime, the capacity can be asymptotically approached by employing antipodal input. It is shown that the AWCN decoder can be applied to additive white Gaussian noise (AWGN) channels with negligible rate loss, while the AWGN decoder when applied to AWCN channels cannot ensure reliable decoding. For the vector receiver case, it is shown that a linear combining receiver front end loses the channel combining gain, a phenomenon drastically different from AWGN vector channels.

Paper Structure

This paper contains 8 sections, 28 equations, 2 figures.

Figures (2)

  • Figure 1: Upper and lower bounds of the AWCN capacity $C$, and $C$ numerically approximated using the Blahut-Arimoto algorithm for discretized input/output alphabets.
  • Figure 2: GMI under AWCN ML decoder in AWGN channels, for different choices of $\lambda^2$.