Sparse Regression Codes for Multi-terminal Source and Channel Coding

We study a new class of codes for Gaussian multi-terminal source and channel coding. These codes are designed using the statistical framework of high-dimensional linear regression and are called Sparse Superposition or Sparse Regression codes. Codewords are linear combinations of subsets of columns of a design matrix. These codes were recently introduced by Barron and Joseph and shown to achieve the channel capacity of AWGN channels with computationally feasible decoding. They have also recently been shown to achieve the optimal rate-distortion function for Gaussian sources. In this paper, we demonstrate how to implement random binning and superposition coding using sparse regression codes. In particular, with minimum-distance encoding/decoding it is shown that sparse regression codes attain the optimal information-theoretic limits for a variety of multi-terminal source and channel coding problems.Comment: 9 pages, appeared in the Proceedings of the 50th Annual Allerton Conference on Communication, Control, and Computing - 201

Practical implementation of identification codes

Identification is a communication paradigm that promises some exponential advantages over transmission for applications that do not actually require all messages to be reliably transmitted, but where only few selected messages are important. Notably, the identification capacity theorems prove the identification is capable of exponentially larger rates than what can be transmitted, which we demonstrate with little compromise with respect to latency for certain ranges of parameters. However, there exist more trade-offs that are not captured by these capacity theorems, like, notably, the delay introduced by computations at the encoder and decoder. Here, we implement one of the known identification codes using software-defined radios and show that unless care is taken, these factors can compromise the advantage given by the exponentially large identification rates. Still, there are further advantages provided by identification that require future test in practical implementations.Comment: submitted to GLOBECOM2

Communicating Correlated Sources Over an Interference Channel

A new coding technique, based on \textit{fixed block-length} codes, is proposed for the problem of communicating a pair of correlated sources over a $2-$user interference channel. Its performance is analyzed to derive a new set of sufficient conditions. The latter is proven to be strictly less binding than the current known best, which is due to Liu and Chen [Dec, 2011]. Our findings are inspired by Dueck's example [March, 1981]