memoryless channel

Strong typicality is often easier to work with in proving theorems for memoryless channels.

The sequence is fed into a discrete memoryless channel (DMC).

In 1956, Claude Shannon introduced the discrete memoryless channel with noiseless feedback.

In information theory, it is common to start with memoryless channels in which the output probability distribution only depends on the current channel input.

Castanie, "Neural networks for modeling nonlinear memoryless channels", Proc.

Channel coding: Discrete memoryless channels, channel capacity, Shannon's second (noisy) coding theorem, error control coding, performance bounds.

Ponson, "Second order fluctuation analysis of a two-layer neural network used for modeling nonlinear memoryless channels", IEEE Trans.

The basic DUDE as described here assumes a signal with a one-dimensional index set over a finite alphabet, a known memoryless channel and a context length that is fixed in advance.

It is the first code with an explicit construction to provably achieve the channel capacity for symmetric binary-input, discrete, memoryless channels (B-DMC) with polynomial dependence on the gap to capacity.

It presents a simple comparison between the discrete sample peak limited memoryless channel and the average power constrained channel when transmitting large numbers of independent, equally spaced, and equal power QPSK signals.

In information theory and signal processing, the Discrete Universal Denoiser (DUDE) is a denoising scheme for recovering sequences over a finite alphabet, which have been corrupted by a discrete memoryless channel.

The AEP for non-stationary discrete-time independent process leads us to (among other results) source coding theorem for non-stationary source (with independent output symbols) and channel coding theorem for non-stationary memoryless channels.

More specifically, the theorem says that there exist codes such that with increasing encoding length the probability of error on a discrete memoryless channel can be made arbitrarily small, provided that the code rate is smaller than the channel capacity.

LDPC codes are capacity-approaching codes, which means that practical constructions exist that allow the noise threshold to be set very close (or even arbitrarily close on the BEC) to the theoretical maximum (the Shannon limit) for a symmetric memoryless channel.