Approaching the Gaussian Channel Capacity With APSK Constellations

Rateless codes approaching capacity of the Gaussian channel

We study codes for the AWGN channel. We build upon the recently proposed sparse superposition codes of Barron and Joseph, which for any constant backoff from capacity can achieve exponential error probability with polynomial decoding time as long as the block-length is greater than a fixed constant (which depends on the backoff). Our contribution is twofold: • First, we give a variant of the co...

Capacity Approaching Signal Constellations for Channels with Memory

Approaching the capacity region of Gaussian wiretap channel with correlated sources by a constant gap

This paper studies the Gaussian wiretap channel with correlated sources available not only at the transmitter and legitimate receiver, but also the eavesdropper. In particular, we are interested in the open problem of finding the optimal auxiliary random variable, which provides a closed-form solution to the secret-message and secret-key capacity region. To do that, we first take a deterministi...

The Optimal Density of Infinite Constellations for the Gaussian Channel

The setting of a Gaussian channel without power constraints is considered. In this setting the codewords are points in an n-dimensional Euclidean space (an infinite constellation). The channel coding analog of the number of codewords is the density of the constellation points, and the analog of the communication rate is the normalized log density (NLD). The highest achievable NLD with vanishing...

On Hierarchical Signal Constellations for the Gaussian Broadcast Channel

With the introduction of digital broadcasting techniques to radio and television, it becomes desirable to have an efficient possibility to reach receivers operating under varying degrees of received signal strength. Cover [2] showed that it is not efficient to do time sharing, that is transmitting a robust but low quality signal time multiplexed with a signal more susceptible to perturbations o...