Ramesh Pyndiah

Bio: Ramesh Pyndiah is an academic researcher from École nationale supérieure des télécommunications de Bretagne. The author has contributed to research in topics: Decoding methods & Block code. The author has an hindex of 18, co-authored 156 publications receiving 2756 citations. Previous affiliations of Ramesh Pyndiah include Philips & École Normale Supérieure.

Near-optimum decoding of product codes: block turbo codes

Abstract: This paper describes an iterative decoding algorithm for any product code built using linear block codes. It is based on soft-input/soft-output decoders for decoding the component codes so that near-optimum performance is obtained at each iteration. This soft-input/soft-output decoder is a Chase decoder which delivers soft outputs instead of binary decisions. The soft output of the decoder is an estimation of the log-likelihood ratio (LLR) of the binary decisions given by the Chase decoder. The theoretical justifications of this algorithm are developed and the method used for computing the soft output is fully described. The iterative decoding of product codes is also known as the block turbo code (BTC) because the concept is quite similar to turbo codes based on iterative decoding of concatenated recursive convolutional codes. The performance of different Bose-Chaudhuri-Hocquenghem (BCH)-BTCs are given for the Gaussian and the Rayleigh channel. Performance on the Gaussian channel indicates that data transmission at 0.8 dB of Shannon's limit or more than 98% (R/C>0.98) of channel capacity can be achieved with high-code-rate BTC using only four iterations. For the Rayleigh channel, the slope of the bit-error rate (BER) curve is as steep as for the Gaussian channel without using channel state information.

Near optimum decoding of product codes

Abstract: A new iterative decoding algorithm for product codes (block) based on soft decoding and soft decision output of the component codes is described in the paper. Monte Carlo simulations of the bit error rate (BER) after decoding using this new algorithm for different product codes indicate coding gains of up to 8 dB. This new coding scheme is attractive for digital transmission systems requiring powerful coding schemes with a high code rate (R>0.8). In the paper the authors compare their coding scheme with one of the best coding schemes, the "turbo-codes", in terms of BER performance.

Method for detecting information bits processed by concatenated block codes

Abstract: The transmitted bits are coded according to the product of at least two systematic block codes. An iterative decoding is applied in order to determine, at each code word search step, a data matrix and a decision matrix which are used for the following step. The new decision matrix is determined at each step by decoding the rows or the columns of the input matrix, and the new data matrix is determined by taking account of correction terms which increase the reliability of the decoding at each iteration. The method is especially suited for use with high-efficiency block codes.

Performance of block turbo coded 16-QAM and 64-QAM modulations

Abstract: We present the results of our investigation concerning the performance of block turbo codes associated with high spectral efficiency QAM modulations. By using a pragmatic approach, simulation results show that all the block turbo coded QAM modulations we have tested are at 2.9/spl plusmn/0.2 dB of their Shannon's limit. The block turbo coded QAM modulations also outperform TCM schemes by at least 1 dB at a BER (bit error rate) of 10/sup -5/. Concerning convolutional turbo coded QAM modulations, the block turbo coded QAM modulations exhibit slightly better performance for spectral efficiencies greater than 4 bits/s/Hz.

Process for transmitting information bits with error correction coding, coder and decoder for the implementation of this process

Abstract: The bits transmitted are coded according to the product of at least two systematic block codes. Iterative decoding is applied in order to determine, at each code word search step, a data matrix ({R}) and a decision matrix ({D}) used for the following step. The new decision matrix is determined at each step by decoding the lines or columns of the input matrix, and the new data matrix is determined taking into account the correction terms which increase the reliability of the decoding on each iteration. The coding and decoding circuits (17) are rendered programmable by a shortening technique allowing selection of the number k-X of non-redundant information bits per block to be coded. Known values are assigned to the other bits, the positions of which are uniformly distributed according to each dimension of the matrices.

Near optimum error correcting coding and decoding: turbo-codes

Abstract: This paper presents a new family of convolutional codes, nicknamed turbo-codes, built from a particular concatenation of two recursive systematic codes, linked together by nonuniform interleaving. Decoding calls on iterative processing in which each component decoder takes advantage of the work of the other at the previous step, with the aid of the original concept of extrinsic information. For sufficiently large interleaving sizes, the correcting performance of turbo-codes, investigated by simulation, appears to be close to the theoretical limit predicted by Shannon.

Iterative decoding of binary block and convolutional codes

Abstract: Iterative decoding of two-dimensional systematic convolutional codes has been termed "turbo" (de)coding. Using log-likelihood algebra, we show that any decoder can be used which accepts soft inputs-including a priori values-and delivers soft outputs that can be split into three terms: the soft channel and a priori inputs, and the extrinsic value. The extrinsic value is used as an a priori value for the next iteration. Decoding algorithms in the log-likelihood domain are given not only for convolutional codes but also for any linear binary systematic block code. The iteration is controlled by a stop criterion derived from cross entropy, which results in a minimal number of iterations. Optimal and suboptimal decoders with reduced complexity are presented. Simulation results show that very simple component codes are sufficient, block codes are appropriate for high rates and convolutional codes for lower rates less than 2/3. Any combination of block and convolutional component codes is possible. Several interleaving techniques are described. At a bit error rate (BER) of 10/sup -4/ the performance is slightly above or around the bounds given by the cutoff rate for reasonably simple block/convolutional component codes, interleaver sizes less than 1000 and for three to six iterations.

Survey on Free Space Optical Communication: A Communication Theory Perspective

Abstract: Optical wireless communication (OWC) refers to transmission in unguided propagation media through the use of optical carriers, i.e., visible, infrared (IR), and ultraviolet (UV) bands. In this survey, we focus on outdoor terrestrial OWC links which operate in near IR band. These are widely referred to as free space optical (FSO) communication in the literature. FSO systems are used for high rate communication between two fixed points over distances up to several kilometers. In comparison to radio-frequency (RF) counterparts, FSO links have a very high optical bandwidth available, allowing much higher data rates. They are appealing for a wide range of applications such as metropolitan area network (MAN) extension, local area network (LAN)-to-LAN connectivity, fiber back-up, backhaul for wireless cellular networks, disaster recovery, high definition TV and medical image/video transmission, wireless video surveillance/monitoring, and quantum key distribution among others. Despite the major advantages of FSO technology and variety of its application areas, its widespread use has been hampered by its rather disappointing link reliability particularly in long ranges due to atmospheric turbulence-induced fading and sensitivity to weather conditions. In the last five years or so, there has been a surge of interest in FSO research to address these major technical challenges. Several innovative physical layer concepts, originally introduced in the context of RF systems, such as multiple-input multiple-output communication, cooperative diversity, and adaptive transmission have been recently explored for the design of next generation FSO systems. In this paper, we present an up-to-date survey on FSO communication systems. The first part describes FSO channel models and transmitter/receiver structures. In the second part, we provide details on information theoretical limits of FSO channels and algorithmic-level system design research activities to approach these limits. Specific topics include advances in modulation, channel coding, spatial/cooperative diversity techniques, adaptive transmission, and hybrid RF/FSO systems.

Unveiling turbo codes: some results on parallel concatenated coding schemes

Abstract: A parallel concatenated coding scheme consists of two simple constituent systematic encoders linked by an interleaver. The input bits to the first encoder are scrambled by the interleaver before entering the second encoder. The codeword of the parallel concatenated code consists of the input bits to the first encoder followed by the parity check bits of both encoders. This construction can be generalized to any number of constituent codes. Parallel concatenated schemes employing two convolutional codes as constituent codes, in connection with an iterative decoding algorithm of complexity comparable to that of the constituent codes, have been previously shown to yield remarkable coding gains close to theoretical limits. They have been named, and are known as, "turbo codes". We propose a method to evaluate an upper bound to the bit error probability of a parallel concatenated coding scheme averaged over all interleavers of a given length. The analytical bounding technique is then used to shed some light on some crucial questions, which have been floating around in the communications community since the proposal of turbo codes.