Javier Garcia-Frias

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.

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.

Joint turbo decoding and estimation of hidden Markov sources

We describe a joint source-channel scheme for modifying a turbo decoder in order to exploit the statistical characteristics of hidden Markov sources. The basic idea is to treat the trellis describing the hidden Markov source as another constituent decoder which exchanges information with the other constituent decoder blocks. The source block uses as extrinsic information the probability of the input bits that is provided by the constituent decoder blocks. On the other hand, it produces a new estimation of such a probability which will be used as extrinsic information by the constituent turbo decoders. The proposed joint source-channel decoding technique leads to significantly improved performance relative to systems in which source statistics are not exploited and avoids the need to perform any explicit source coding prior to transmission. Lack of a priori knowledge of the source parameters does not degrade the performance of the system, since these parameters can be jointly estimated with turbo decoding.

Compression of binary memoryless sources using punctured turbo codes

In this letter, we propose the use of punctured turbo codes to perform near-lossless compression and joint source-channel coding of binary memoryless sources. Compression is achieved by puncturing the turbo code to the desired rate. No information about the source distribution is required in the encoding process. Moreover, the source parameters do not need to be known in the decoder, since they can be estimated jointly with the iterative decoding process.

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.

Joint source-channel decoding of correlated sources over noisy channels

We consider the case of two correlated binary information sequences. Instead of compressing the information using source coding, both sequences are independently channel encoded, and transmitted over an AWGN channel. 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.

LDGM codes for channel coding and joint source-channel coding of correlated sources

We propose a coding scheme based on the use of systematic linear codes with low-density generator matrix (LDGM codes) for channel coding and joint source-channel coding of multiterminal correlated binary sources. In both cases, the structures of the LDGM encoder and decoder are shown, and a concatenated scheme aimed at reducing the error floor is proposed. Several decoding possibilities are investigated, compared, and evaluated. For different types of noisy channels and correlation models, the resulting performance is very close to the theoretical limits.

Analog Joint Source-Channel Coding Using Non-Linear Curves and MMSE Decoding

We investigate the performance of a discrete-time all-analog-processing joint source-channel coding system for the transmission of memoryless sources over average power constrained AWGN channels. First, N:1 bandwidth compression systems are analyzed and optimized. At the encoder, N samples of an i.i.d. source are directly mapped into one channel symbol using a non-linear curve. Different from previous work in the literature, we introduce an additional degree of freedom at the encoder, MMSE decoding instead of ML decoding is considered, and we focus on both high and low channel signal-to-noise ratio (CSNR) regions. By using MMSE decoding, the proposed system presents a performance very close to the theoretical limits, even at low CSNR, as long as the system parameters are properly optimized. Then, N:K bandwidth compression systems are constructed by parallel combination of an M:1 system and a 1:1 uncoded system, and the optimal power allocation between the two constituent systems is derived in order to maximize the overall output signal-to-distortion ratio (SDR). Finally, 1:2 bandwidth expansion systems using mapping functions similar to those used in 2:1 system are investigated. Different from digital systems, the proposed scheme does not require long block lengths to achieve good performance, and shows graceful degradation when the CSNR is lower than the one used for the design.

Combining hidden Markov source models and parallel concatenated codes

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.

Hidden Markov models for burst error characterization in indoor radio channels

Many digital communication channels exhibit statistical dependencies among errors. The design of error control schemes for such channels and their performance evaluation is simplified if appropriate generative models of the overall communication link are available. This paper presents a new class of generative models based on the interconnection of hidden Markov submodels parameterized by the Baum-Welch algorithm. The method has some resemblance to the well-studied problem of speech recognition of isolated words; however, in our approach, instead of dealing with words, one deals with error bursts, and the final goal is to generate bursts rather than to recognize words. The proposed model is particularly suitable for simulating error profiles with long bursts, as is often the case in indoor radio channels, where the error-free gaps inside a burst are heavily nonrenewal. The merits of the method are corroborated by applying the technique to two particular examples of indoor code-division multiple-access (CDMA) radio links.