Serially-Concatenated Low-Density Generator Matrix (SCLDGM) Codes for Transmission Over AWGN and Rayleigh Fading Channels

Low density generator matrix (LDGM) codes are a particular class of low density parity check (LDPC) codes with very low encoding complexity. Single LDGM codes present high error-floors, which can be substantially reduced with the serial concatenation of two LDGM (SCLDGM) codes. We propose a technique to obtain good SCLDGM codes using extrinsic information transfer (EXIT) functions in a novel way. Although the optimization is performed for AWGN channels with binary signaling, the resulting codes are also optimal for AWGN and perfectly-interleaved Rayleigh fading channels with non-binary signaling and perfect CSI at reception, provided that Gray mapping is utilized. Optimized regular and irregular SCLDGM codes outperform heuristically-designed LDGM codes existing in the literature, and have a performance similar to or better than that of irregular repeat accumulate (IRA) codes.     

Turbo-Like Codes for Transmission of Correlated Sources over Noisy Channels

In this article we focus on distributed coding of correlated sources that are transmitted through either separated noisy channels (one per source) or over a multiple access channel (MAC). For simplicity, we consider only two sources, but the proposed approach can be easily extended to the case of more sources. The two sources are encoded independently of each other (i.e., for a given source neither the realization from the other source nor the correlation model are available at the encoder site) and transmitted through the channel. Then, the correlation between the sources is exploited at a common receiver, aiming at the reconstruction of the two sources. Although this problem has many practical applications e.g., in the case of sensor networks (where correlated data has to be communicated between the different nodes) and video/image applications, we will focus in the key ideas without discussing specific applications. For simplicity, we will assume simple correlation models and binary sources.     

Combining hidden Markov source models and parallel concatenatedcodes

We present here a framework for modifying a decoder for parallel concatenated codes to incorporate a general hidden Markov source model. This allows the receiver to utilize the statistical characteristics of the source during the decoding process, and leads to significantly improved performance relative to systems in which source statistics are not exploited. One of the constituent decoders makes use of a modified trellis which jointly describes the source and the encoder. The number of states in this modified trellis is the product of the number of states in the hidden Markov source and the number of states in the encoder     

Compression of correlated binary sources using turbo codes

We propose the use of punctured turbo codes for compression of correlated binary sources. Compression is achieved because of puncturing. The resulting performance is close to the theoretical limit provided by the Slepian-Wolf (1973) theorem. No information about the correlation between sources is required in the encoding process. The proposed source decoder utilizes iterative schemes, and performs well even when the correlation between the sources is not known in the decoder, since it can be estimated jointly with the iterative decoding process     

Results of channel error profiles for DECT

This letter presents the main statistical characterization of the underlying error process obtained in the case of the Digital European Cordless Telecommunications (DECT) radio system. By simulation of the transmission Link, error sequences are generated for different channel parameters. Relevant statistics are then computed for the purpose of efficient channel coding design and evaluation     

Stochastic context-free grammars and hidden Markov models for modeling of bursty channels

In order to design good error-control schemes for bursty channels and also to facilitate performance analysis, it is important to develop accurate and simple statistical channel error models for the channels of interest. We propose two novel generative methods to model the end-to-end error profile of radio channels described by long well-defined error bursts interleaved with long error-free intervals. The first method makes use of the power of stochastic context-free grammars to model palindromes. The second utilizes simple hidden Markov models with specific structures, which are suggested by the ideas presented in the first method. Both methods achieve much better performance than previously proposed approaches without introducing more complexity. Although the complexity of the second method is slightly greater than that of the first, its advantage is that it can be easily applied in decoding implementations specifically tailored to deal with bursty channels.     

Turbo decoders for Markov channels

We present a decoder for parallel concatenated codes that incorporates a binary-input binary-output Markov channel model, thereby allowing the receiver to utilize the statistical structure of the channel during the decoding process. These decoders can enable reliable communication at rates which are above the capacity of a memoryless channel with the same stationary bit error probability as the Markov channel, and therefore outperform systems based on the traditional approach of using a channel interleaver to create a channel which is assumed to be memoryless     

Approaching Shannon performance by iterative decoding of linear codes with low-density generator matrix

We propose the use of linear codes with low density generator matrix to achieve a performance similar to that of turbo and standard low-density parity check codes. The use of iterative decoding techniques - message passing -over the corresponding graph achieves a performance close to the Shannon theoretical limit. As an advantage with respect to turbo and standard low-density parity check codes, the complexity of the decoding and encoding procedures is very low.     

Rate-compatible low-density generator matrix codes

We propose a family of rate-compatible codes based on the concatenation of two linear codes with low-density generator matrix, which are a special class of LDPC codes with low encoding complexity. The proposed scheme is characterized by its simplicity of construction, and does not require optimization of the puncturing pattern.     

Near-Shannon/Slepian-Wolf performance for unknown correlated sources over AWGN channels

We consider the problem of joint source-channel coding of two correlated binary information sequences. Instead of compressing the information using source coding, both sequences are independently channel encoded and transmitted over two independent additive white Gaussian noise channels. No information about the correlation between the sources is required in the encoding process. The correlation between both sequences is exploited at the receiver, allowing reliable communications at signal-to-noise ratios very close to the theoretical limits established by the combination of Shannon and Slepian-Wolf theorems. This occurs even when the correlation between sources is not known at the decoder, since it can be estimated jointly with the iterative decoding process.