Group, Lattice and Polar Codes for Multi-terminal Communications.

We study the performance of algebraic codes for multi-terminal communications. This thesis consists of three parts: In the rst part, we analyze the performance of group codes for communications systems. We observe that although group codes are not optimal for point-to-point scenarios, they can improve the achievable rate region for several multi-terminal communications settings such as the Distributed Source Coding and Interference Channels. The gains in the rates are particularly signicant when the structure of the source/channel is matched to the structure of the underlying group. In the second part, we study the continuous alphabet version of group/linear codes, namely lattice codes. We show that similarly to group codes, lattice codes can improve the achievable rate region for multi-terminal problems. In the third part of the thesis, we present coding schemes based on polar codes to practically achieve the performance limits derived in the two earlier parts. We also present polar coding schemes to achieve the known achievable rate regions for multi-terminal communications problems such as the Distributed Source Coding, the Multiple Description Coding, Broadcast Channels, Interference Channels and Multiple Access Channels.PhDElectrical Engineering: SystemsUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/108876/1/ariaghs_1.pd

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

Labeling Diversity for 2x2 WLAN Coded-Cooperative Networks

Labelling diversity is an efficient technique recently proposed in the literature and aims to improve the bit error rate(BER) performance of wireless local area network (WLAN) systems with two transmit and two receive antennas without increasing the transmit power and bandwidth requirements. In this paper, we employ labelling diversity with different space-time channel codes such as convolutional, turbo and low density parity check (LDPC) for both point-to-point and coded-cooperative communication scenarios. Joint iterative decoding schemes for distributed turbo and LDPC codes are also presented. BER performance bounds at an error floor (EF) region are derived and verified with the help of numerical simulations for both cooperative and non-cooperative schemes. Numerical simulations show that the coded-cooperative schemes with labelling diversity achieve better BER performances and use of labelling diversity at the source node significantly lowers relay outage probability and hence the overall BER performance of the coded-cooperative scheme is improved manifolds

Achieving Marton's Region for Broadcast Channels Using Polar Codes

This paper presents polar coding schemes for the 2-user discrete memoryless broadcast channel (DM-BC) which achieve Marton's region with both common and private messages. This is the best achievable rate region known to date, and it is tight for all classes of 2-user DM-BCs whose capacity regions are known. To accomplish this task, we first construct polar codes for both the superposition as well as the binning strategy. By combining these two schemes, we obtain Marton's region with private messages only. Finally, we show how to handle the case of common information. The proposed coding schemes possess the usual advantages of polar codes, i.e., they have low encoding and decoding complexity and a super-polynomial decay rate of the error probability. We follow the lead of Goela, Abbe, and Gastpar, who recently introduced polar codes emulating the superposition and binning schemes. In order to align the polar indices, for both schemes, their solution involves some degradedness constraints that are assumed to hold between the auxiliary random variables and the channel outputs. To remove these constraints, we consider the transmission of $k$ blocks and employ a chaining construction that guarantees the proper alignment of the polarized indices. The techniques described in this work are quite general, and they can be adopted to many other multi-terminal scenarios whenever there polar indices need to be aligned.Comment: 26 pages, 11 figures, accepted to IEEE Trans. Inform. Theory and presented in part at ISIT'1