On LDPC Codes for Gaussian Interference Channels

In this paper, we focus on the two-user Gaussian interference channel (GIC), and study the Han-Kobayashi (HK) coding/decoding strategy with the objective of designing low-density parity-check (LDPC) codes. A code optimization algorithm is proposed which adopts a random perturbation technique via tracking the average mutual information. The degree distribution optimization and convergence threshold computation are carried out for strong and weak interference channels, employing binary phase-shift keying (BPSK). Under strong interference, it is observed that optimized codes operate close to the capacity boundary. For the case of weak interference, it is shown that via the newly designed codes, a nontrivial rate pair is achievable, which is not attainable by single user codes with time-sharing. Performance of the designed LDPC codes are also studied for finite block lengths through simulations of specific codes picked from the optimized degree distributions.Comment: ISIT 201

Design of LDPC Codes for Two-Way Relay Systems with Physical-Layer Network Coding

Cataloged from PDF version of article.This letter presents low-density parity-check (LDPC) code design for two-way relay (TWR) systems employing physical-layer network coding (PLNC). We focus on relay decoding, and propose an empirical density evolution method for estimating the decoding threshold of the LDPC code ensemble. We utilize the proposed method in conjunction with a random walk optimization procedure to obtain good LDPC code degree distributions. Numerical results demonstrate that the specifically designed LDPC codes can attain improvements of about 0.3 dB over off-the-shelf LDPC codes (designed for point-to-point additive white Gaussian noise channels), i.e., it is new code designs are essential to optimize the performance of TWR systems

Short block length code design for interference channels

We focus on short block length code design for Gaussian interference channels (GICs) using trellis-based codes. We employ two different decoding techniques at the receiver side, namely, joint maximum likelihood (JML) decoding and single user (SU) minimum distance decoding. For different interference levels (strong and weak) and decoding strategies, we derive error-rate bounds to evaluate the code performance. We utilize the derived bounds in code design and provide several numerical examples for both strong and weak interference cases. We show that under the JML decoding, the newly designed codes offer significant improvements over the alternatives of optimal point-to-point (P2P) trellis-based codes and off-the-shelf low density parity check (LDPC) codes with the same block lengths. © 2016 IEEE

MRI RECONSTRUCTION WITH ANALYSIS SPARSE REGULARIZATION UNDER IMPULSIVE NOISE

24th European Signal Processing Conference (EUSIPCO) -- AUG 28-SEP 02, 2016 -- Budapest, HUNGARY -- European Assoc Signal ProcWe will be considering analysis sparsity based regularization for Magnetic Resonance Imaging reconstruction. The analysis sparsity regularization is based on the recently introduced Transform Learning framework, which has reduced complexity regarding other sparse regularization methods. We will formulate a variational reconstruction problem which utilizes the analysis sparsity regularization together with an l(1) norm based data fidelity term. The use of the non-smooth data fidelity term results in robustness against outliers and impulsive noise in the observed data. The resulting algorithm with the l(1) observation fidelity showcases enhanced performance under impulsive observation noise when compared to a similar algorithm utilizing the conventional quadratic error term.WOS:0003918919001052-s2.0-8500605636

TRANSFORM LEARNING MRI WITH GLOBAL WAVELET REGULARIZATION

23rd European Signal Processing Conference (EUSIPCO) -- AUG 31-SEP 04, 2015 -- Nice, FRANCE -- EURECOMSparse regularization of the reconstructed image in a transform domain has led to state of the art algorithms for magnetic resonance imaging (MRI) reconstruction, Recently, new methods have been proposed which perform sparse regularization on patches extracted from the image. These patch level regularization methods utilize synthesis dictionaries or analysis transforms learned from the patch sets. In this work we jointly enforce a global wavelet domain sparsity constraint together with a patch level, learned analysis sparsity prior. Simulations indicate that this joint regularization culminates in MRI reconstruction performance exceeding the performance of methods which apply either of these terms alone.WOS:0003779438003732-s2.0-8496394396

MRI reconstruction with joint global regularization and transform learning

Sparsity based regularization has been a popular approach to remedy the measurement scarcity in image reconstruction. Recently, sparsifying transforms learned from image patches have been utilized as an effective regularizer for the Magnetic Resonance Imaging (MRI) reconstruction. Here, we infuse additional global regularization terms to the patch-based transform learning. We develop an algorithm to solve the resulting novel cost function, which includes both patchwise and global regularization terms. Extensive simulation results indicate that the introduced mixed approach has improved MRI reconstruction performance, when compared to the algorithms which use either of the patchwise transform learning or global regularization terms alone. (C) 2016 Elsevier Ltd. All rights reserved.WOS:0003839326000012-s2.0-84982804438PubMed: 2751321

LDPC Code Design for Binary-Input Binary-Output Z Interference Channels

IEEE International Symposium on Information Theory (ISIT) -- JUN 14-19, 2015 -- Hong Kong, PEOPLES R CHINA -- IEEE, IEEE Informat Theory Soc, HUAWEI, VTECH, Qualcomm, Google, Croucher Fdn, IBM, BROADCOM, Mediatek, iSN State Key Lab, INC, ITS, SENG, Hong Kong Polytechn Univ, K C Wong Educ Fdn, NSF, Hong Kong Univ Sci & Technol, Sch EngnIn this paper, we explore code optimization for two-user discrete memoryless interference channels (DMICs) wherein the inputs and outputs of the channel are from a finite alphabet. For encoding, we employ irregular low-density parity-check (LDPC) codes combined with non-linear trellis codes (NLTCs) to satisfy the desired distribution of zeros and ones in the transmitted codewords. At the receiver sides, we adopt BCJR algorithm based decoders to compute the symbol-by symbol log-likelihood ratios (LLRs) of LDPC coded bits to be fed to message passing decoders. As a specific example, we consider the binary-input binary-output Z interference channel (BIBO ZIC) for which the transmitted and received signals are binary and one of the receivers is interference free. For a specific example of a BIBO ZIC, we examine the Han-Kobayashi inner bound on the achievable rate pairs and show that with a simple scheme of sending the messages as private one can achieve the sum-capacity of the channel. We also perform code optimization and demonstrate that the jointly optimized codes outperform the optimal single user codes with time sharing.WOS:0003809047010282-s2.0-8496979044

LDPC Code Design for the Two-User Gaussian Multiple Access Channel

We study code design for two-user Gaussian multiple access channels (GMACs) under fixed channel gains and under quasi-static fading. We employ low-density parity-check (LDPC) codes with BPSK modulation and utilize an iterative joint decoder. Adopting a belief propagation (BP) algorithm, we derive the PDF of the log-likelihood-ratios (LLRs) fed to the component LDPC decoders. Via examples, it is illustrated that the characterized PDF resembles a Gaussian mixture (GM) distribution, which is exploited in predicting the decoding performance of LDPC codes over GMACs. Based on the GM assumption, we propose variants of existing analysis methods, named modified density evolution (DE) andmodified extrinsic information transfer (EXIT). We derive a stability condition on the degree distributions of the LDPC code ensembles and utilize it in the code optimization. Under fixed channel gains, the newly optimized codes are shown to perform close to the capacity region boundary outperforming the existing designs and the off-the-shelf point-to-point (P2P) codes. Under quasi-static fading, optimized codes exhibit consistent improvements upon the P2P codes as well. Finite block length simulations of specific codes picked from the designed ensembles are also carried out and it is shown that optimized codes perform close to the outage limits.National Science FoundationNational Science Foundation (NSF) [NSF-CCF 1117174]; European CommissionEuropean CommissionEuropean Commission Joint Research Centre [MC-CIG PCIG12-GA-2012-334213]; Turkish Scientific and Technological Research Council of Turkey (TUBITAK)Turkiye Bilimsel ve Teknolojik Arastirma Kurumu (TUBITAK) [114E601]This work was supported in part by the National Science Foundation under Grant NSF-CCF 1117174, in part by the European Commission under Grant MC-CIG PCIG12-GA-2012-334213, and in part by the Turkish Scientific and Technological Research Council of Turkey (TUBITAK) under Grant 114E601. The associate editor coordinating the review of this paper and approving it for publication was Z. Wang.WOS:0003742405000302-s2.0-8496385740