14 - Gaussian channel and Shannon–Hartley theorem

Summary

This chapter considers the continuous-channel case represented by the Gaussian channel, namely, a continuous communication channel with Gaussian additive noise. This will lead to a fundamental application of Shannon's coding theorem, referred to as the Shannon–Hartley theorem (SHT), another famous result of information theory, which also credits the earlier 1920 contribution of Ralph Hartley, who derived what remained known as the Hartley's law of communication channels. This theorem relates channel capacity to the signal and noise powers, in a most elegant and simple formula. As a recent and little-noticed development in this field, I will describe the nonlinear channel, where the noise is also a function of the transmitted signal power, owing to channel nonlinearities (an exclusive feature of certain physical transmission pipes, such as optical fibers). As we shall see, the modified SHT accounting for nonlinearity represents a major conceptual progress in information theory and its applications to optical communications, although its existence and consequences have, so far, been overlooked in textbooks. This chapter completes our description of classical information theory, as resting on Shannon's works and founding theorems. Upon completion, we will then be equipped to approach the field of quantum information theory, which represents the second part of this series of chapters.

Gaussian channel

Referring to Chapter 6, a continuous communications channel assumes a continuous originator source, X, whose symbol alphabet x₁,…, x_i can be viewed as representing time samples of a continuous, real variable x, which is associated with a continuous probability distribution function or PDF, p(x).