The fundamental limits of communication over a noisy channel, in particular, over an AWGN channel, are described, and channel coding is introduced as a way of approaching the ultimate information-theoretic limits of reliable communication. Linear block codes and convolutional codes are studied in some depth. Encoding and decoding algorithms, as well as basic performance analysis results, are developed. The Viterbi algorithm is introduced for both hard-decision decoding and soft-decision decoding of convolutional codes.

In Chapter 19 we apply methods developed in the previous chapters (namely the weak converse and the random/maximal coding achievability) to compute the channel capacity. This latter notion quantifies the maximal amount of (data) bits that can be reliably communicated per single channel use in the limit of using the channel many times. Formalizing the latter statement will require introducing the concept of a communication channel. Then for special kinds of channels (the memoryless and the information-stable ones) we will show that computing the channel capacity reduces to maximizing the (sequence of the) mutual information. This result, known as Shannon’s noisy channel coding theorem, is very special as it relates the value of a (discrete, combinatorial) optimization problem over codebooks to that of a (convex) optimization problem over information measures. It builds a bridge between the abstraction of information measures (Part I) and practical engineering problems.

The so-called magnetic communication makes use of the time-varying magnetic field produced by the transmitting antenna, so that the receiving antenna receives the energy signal by mutual inductance. Research studies show that the penetrability of a magnetic communication system depends on the magnetic permeability of the medium. Because the magnetic permeability of the layer, rock, ice, soil, and ore bed is close to that of the air, channel conditions have less effects on magnetic transmission than electric transmission. Therefore, the communication network based on deep-penetrating MI can expand the perception ability and sensing range of information technology effectively, which can be applied to complex environments such as underground, underwater, tunnel, mountain, rock, ice, and forest. We conclude that the network construction of IoT based on magnetic communication is of great value and can be regarded as one of the reliable technologies to improve the connectivity of a wireless network.