In information theory, the noisy-channel coding theorem (sometimes Shannon's theorem or Shannon's limit), establishes that for any given degree of noise
noisy-channel coding theorem to the archetypal case of a continuous-time analog communications channel subject to Gaussian noise. The theorem establishes
In information theory, Shannon's source coding theorem (or noiseless coding theorem) establishes the statistical limits to possible data compression for
over a communication channel. Following the terms of the noisy-channel coding theorem, the channel capacity of a given channel is the highest information
applied to varied communication channels such as telephone lines or disk drive storage. The noisy-channel coding theorem applies to BSCp, saying that information
telecommunication, information theory, and coding theory, forward error correction (FEC) or channel coding is a technique used for controlling errors
There are four types of coding: Data compression (or source coding) Error control (or channel coding) Cryptographic coding Line coding Data compression attempts
the direct coding theorem and the converse theorem. The direct coding theorem demonstrates that the quantum mutual information of the channel is an achievable
given by the noisy-channel coding theorem; the practical result of the Shannon–Hartley law for the channel capacity of a Gaussian channel; and of course the
source coding theorem for non-stationary source (with independent output symbols) and noisy-channel coding theorem for non-stationary memoryless channels. T