Browsing by Subject "Iterative decoding"
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Item Open Access Implementing the Han-Kobayashi scheme using low density parity check codes over Gaussian interference channels(Institute of Electrical and Electronics Engineers Inc., 2015) Sharifi S.; Tanc, A. K.; Duman, T. M.We focus on Gaussian interference channels (GICs) and study the Han-Kobayashi coding strategy for the two-user case with the objective of designing implementable (explicit) channel codes. Specifically, low-density parity-check codes are adopted for use over the channel, their benefits are studied, and suitable codes are designed. Iterative joint decoding is used at the receivers, where independent and identically distributed channel adapters are used to prove that log-likelihood-ratios exchanged among the nodes of the Tanner graph enjoy symmetry when BPSK or QPSK with Gray coding is employed. This property is exploited in the proposed code optimization algorithm adopting a random perturbation technique. Code optimization and convergence threshold computations are carried out for different GICs employing finite constellations by tracking the average mutual information. Furthermore, stability conditions for the admissible degree distributions under strong and weak interference levels are determined. Via examples, it is observed that the optimized codes using BPSK or QPSK with Gray coding operate close to the capacity boundary for strong interference. For the case of weak interference, it is shown that nontrivial rate pairs are achievable via the newly designed codes, which are not possible by single user codes with time sharing. Performance of the designed codes is also studied for finite block lengths through simulations of specific codes picked with the optimized degree distributions with random constructions, where, for one instance, the results are compared with those of some structured designs. © 1972-2012 IEEE.Item Open Access LDPC code design for the two-user Gaussian multiple access channel(Institute of Electrical and Electronics Engineers Inc., 2016) Sharifi S.; Tanc, A. K.; Duman, T. M.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) and modified 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. © 2015 IEEE.Item Open Access Linear MMSE-optimal turbo equalization using context trees(IEEE, 2013) Kim, K.; Kalantarova, N.; Kozat, S. S.; Singer, A. C.Formulations of the turbo equalization approach to iterative equalization and decoding vary greatly when channel knowledge is either partially or completely unknown. Maximum aposteriori probability (MAP) and minimum mean-square error (MMSE) approaches leverage channel knowledge to make explicit use of soft information (priors over the transmitted data bits) in a manner that is distinctly nonlinear, appearing either in a trellis formulation (MAP) or inside an inverted matrix (MMSE). To date, nearly all adaptive turbo equalization methods either estimate the channel or use a direct adaptation equalizer in which estimates of the transmitted data are formed from an expressly linear function of the received data and soft information, with this latter formulation being most common. We study a class of direct adaptation turbo equalizers that are both adaptive and nonlinear functions of the soft information from the decoder. We introduce piecewise linear models based on context trees that can adaptively approximate the nonlinear dependence of the equalizer on the soft information such that it can choose both the partition regions as well as the locally linear equalizer coefficients in each region independently, with computational complexity that remains of the order of a traditional direct adaptive linear equalizer. This approach is guaranteed to asymptotically achieve the performance of the best piecewise linear equalizer, and we quantify the MSE performance of the resulting algorithm and the convergence of its MSE to that of the linear minimum MSE estimator as the depth of the context tree and the data length increase.Item Open Access Special Issue on Advances in Channel Coding(Korean Institute of Communication Sciences, 2015) Arikan, E.; Lentmaier, M.; Montorsi, G.Since the invention of turbo codes in 1993 there has been an enormous interest and progress in the field of capacity approaching code constructions. Many classical constructions have been replaced by newer, better performing codes with feasible decoding complexity. Most of these modern code constructions, such as turbo codes, Gallager's low-density parity-check (LDPC) codes and their generalizations, can be modeled by sparse graphical models. Spatial coupling of sparse graphical models has in the last years attracted a lot of interest due to the threshold saturation phenomenon, which leads to capacity achieving performance with iterative message passing decoding. Polar codes are a recently discovered class of capacity achieving codes that are formed by an explicit construction based on a phenomenon called channel polarization. These codes, too, have various low-complexity decoding algorithms based on message passing on a sparse graph that has a recursive structure similar to that of fast transforms in signal processing.Item Open Access Turbo/katlamalı kodlanmış SFBC/STBC-OFDM sistemlerinde yinelemeli kanal kestirimi(2006-04) Doǧan, H.; Çırpan, H. A.; Panayırcı, ErdalWe consider the design of turbo receiver structures for space-frequency block coded (SFBC/STBC) OFDM systems in unknown frequency selective fading channels. The Turbo receiver structures for SFBC/STBC-OFDM systems under consideration consists of an iterative MAP Expectation/Maximization (EM) channel estimation algorithm, soft MMSE SFBC/STBC decoder and a soft MAP outer-channel-code decoder. MAP-EM employs iterative channel estimation and it improves receiver performance by re-estimating the channel after each decoder iteration. Moreover, the MAP-EM approach considers the channel variations as random processes and applies the Karhunen-Loeve (KL) orthogonal series expansion. The performance of the proposed approaches are studied in terms of mean square error and bit-error rate. Through experimental results, the effect of a pilot spacing on the channel estimator performance and sensitivity of turbo receiver structures on channel estimation error are studied. © 2006 IEEE.