This paper discusses the application of Analog Joint Source Channel Coding (JSCC) to Non-Linear Channels, including underwater acoustic channels. Different from traditional digital communication systems, which utilize a source code followed by a channel code, Analog JSCC systems perform coding via a single mapping. We will focus on a particular class of analog codes called space filling curves. The underwater acoustic channel is typically non-linear with Inter-Symbol Interference (ISI). We will first study a simplified version assuming no ISI, developing a scheme to adapt space filling curves to this environment, and studying the theoretical limits. Then, we will extend the analysis to the end-to-end acoustic channel (including ISI), discussing the proposed system in this case. Finally, we will present simulation results of the proposed communication system for the simplified (no ISI) and end-to-end channels.

Social relations reveal the interpersonal association of human beings. Developing techniques to automatically recognize social relations from visual data has great potential for improving human-computer interaction. In this paper, a hybrid deep network is proposed to predict the social relations between two human beings in an image. Unlike existing methods that typically learn deep learning models from scratch, a VGG-FACE model previously trained for face recognition is fine-tuned on a social relation database and used as branches of a siamese-like network. Moreover, a deep network is proposed to extract scene features that contain high-level information related to social relations from whole images and its predictions are fused with the predictions of the siamese network to generate the final result. Experiments show that the proposed approach saves the effort of pre-training and preparing auxiliary datasets, i.e. facial attribute datasets, and outperforms state-of-the-art methods.

We investigate the use of discrete-time analog joint source channel coding (JSCC) in an amplify-and-forward two-hop wireless network subject to Rayleigh fading channels. The discrete-time analog source is compressed using either 2:1, 3:1 or 4:1 low delay dimension-compression parametric and non-parametric analog JSCC. Our results show that both, non-parametric and parametric, analog schemes outperform an ideal fully digital system based on scalar quantization, while the performance obtained with non parametric mappings is superior to that achieved with parametric systems.

Joint source-channel coding for discrete-time analog sources is an appealing transmission approach because of its extremely low delay and complexity. When the users access the channel orthogonally, analog transmission of correlated information over fading multiple access channels (MACs) using modulo-like mappings provides better performance than uncoded transmission. In this paper, we propose a simplified decoder for modulo mappings in possibly non-orthogonal MAC scenarios with a single-antenna users and a multiple-antenna receiver. Sphere decoding is investigated to reduce the computational complexity when the number of users is large. In addition, affordable strategies are proposed to optimize the mapping parameters according to the channel conditions and the source correlation. The obtained results show that the use of modulo mappings is suitable when the number of antennas at the receiver is larger than the number of users and for high correlation between user data.

Analog transmission of correlated information over Gaussian broadcast channels (BCs) using analog joint source- channel coding (JSCC) is addressed. This communication strategy has attractive advantages, such as low complexity, minimal delay, graceful degradation, and adaptation to time-varying environments. In this paper, we focus on the optimization of parametric continuous mappings that satisfy individual quality of service requirements over Gaussian BCs. This optimization is based on the balancing of the user distortions to ensure the feasibility of the resulting optimization problems. An analysis of the overall distortion corresponding to the considered mappings is carried out to design algorithms that determine the optimal values for the mappings parameters. Results show that the proposed analog JSCC scheme provides near optimal performance and an adequate balancing of the individual distortions.

We address the transmission of bivariate Gaussian sources using analog Joint Source Channel Coding (JSCC). The analog mappings are specifically designed to exploit the correlation between the source symbols. A parametric mapping based on sinusoidal functions is proposed and its performance is compared to that of the optimal non parametric mappings and other applicable analog JSCC mappings, and also to the theoretical bound. The obtained results show that the performance of the parametric mapping closely approaches the optimal performance, and it is very similar to that of the non parametric one.

We address the evaluation of low-complexity analog Joint Source Channel Coding (JSCC) methods for the transmission of discrete-time analog symbols over Multiple-Input Multiple-Output (MIMO) Multiple Access Channels (MAC). Analog JSCC is employed to encode the source information at each transmitter prior to be directly input to the MAC access scheme. Three channel access methods are considered to ensure the receiver is able to recover the user information: Code Division Multiple Access (CDMA), linear MMSE access codes and opportunistic access. CDMA allows the orthogonal transmission of the user data requiring only Channel State Information (CSI) at reception. On the other hand, linear MMSE access codes exploit CSI knowledge at transmission and exhibit better performance. Finally, opportunistic access also exploits CSI at transmission and allocates all MAC resources to the user with the strongest channel. This latter access scheme exhibits the best performance in terms of sum distortion although it may lead to unfair rate distributions among users.

We address the transmission of discrete-time analog samples over a block fading Multiple Access Channel (MAC). We propose two practical access schemes to achieve the sum capacity of a MAC channel: opportunistic and Code Division Multiple Access (CDMA). The first one is optimal when the Channel State Information (CSI) from all users is available at all the transmitters. However, it can lead to an unfair distribution of the power and transmission rate for each user. On the other hand, the CDMA access scheme allows setting individual rate constraints while showing excellent performance when transmitting either uncompressed or compressed analog Gaussian samples.

We consider the problem of transmission of independent and correlated Gaussian sources over the two user Gaussian Broadcast Channel (GBC). We present a low complexity, low delay, analog joint source channel coding communication system based on extensions of existing Nested Quantization techniques. Simulations results show that the resulting performance is close to the optimal theoretical limits for several cases of interest.

We investigate the performance of analog joint source channel coding systems based on the use of spiral-like space filling curves for the transmission of Gaussian sources over non-linear channels. The non-linearity proceeds from a non-linear power amplifier in the transmitter that exhibits saturation effects at the extremes and also near the origin (see Figure below). Then, the output of the amplifier is sent through an AWGN channel which introduces attenuation that depends on the distance between the transmitter and the receiver. This means that the attenuation cannot be subsumed into the noise variance.

We consider the use of spatial diversity to improve the performance of analog joint source-channel coding in wireless fading channels. The communication system analyzed in this paper consists of discrete-time all-analog-processing joint source-channel coding where Maximum Likelihood (ML) and Minimum Mean Square Error (MMSE) detection are employed. By assuming a fast-fading Rayleigh channel, we show that MMSE performs much better than ML at high Channel Signal-to-Noise Ratios (CSNR) in single-antenna wireless systems. However, such performance gap can be significantly reduced by using multiple receive antennas, thus making low complexity ML decoding very attractive in the case of receive diversity. Moreover, we show that the analog scheme can be considerably robust to imperfect channel estimation. In addition, as an alternative to multiple antennas, we also consider spatial diversity through cooperative communications, and show that the application of the Amplify-and-Forward (AF) protocol with single antenna nodes leads to similar results than when two antennas are available at the receiver and Maximal Ratio Combining (MRC) is applied. Finally, we show that the MMSE implementation of the analog scheme performs very close to the unconstrained capacity of digital schemes using scalar quantization, while its complexity is much lower than that of capacity-approaching digital systems.

The effect of prior knowledge when linear analog codes are used as joint source-channel codes for sources modeled as multivariate Gaussian processes is analyzed. We use information theoretic tools to evaluate the achievable performance gain obtained by exploiting prior knowledge. In order to assess the validity of linear codes in practical scenarios, where exact source statistics are not known, we study the effect of having partial knowledge of the statistics. We model the mismatch of the statistics as an additive perturbation matrix between the real covariance matrix and the postulated covariance matrix in the recovery process. In this setting, we obtain closed form expressions for a deterministic perturbation matrix and using random matrix theory tools we characterize the performance loss for i.i.d. random matrices.

Compressed sampling (CS) is a technique that enables signal reconstruction at sub-Nyquist sampling rate. A key problem in CS is how to design the sampling scheme. In this paper, we propose a novel sampling method for compressed image sampling, which exploits a priori information and uses a block-based strategy to improve image reconstruction. Our block-based sampling scheme assigns more samples to blocks with more high-frequency contents while making sure that important coefficients of each block are sampled. Simulation results show that our proposed method outperforms existing methods on both reconstruction quality and running time.

We propose a novel block-based image transmission system, which exploits the a prior information existing in the DCT domain of images and combines both linear and non-linear coding schemes accommodated to a block-based DCT domain compressed sampling method. An image is firstly divided into blocks and each block is separately sampled in DCT domain. Different coding schemes are used to transmit the samples based on their properties. With block-based strategy, each image block can be processed and transmitted separately, which reduces a lot of latency. Besides, an efficient system optimization algorithm is proposed by jointly optimizing the power allocation scheme and the transmission parameters to search for the maximum peak signal-to-noise ratio (PSNR) of the reconstructed image. Simulation results show that the proposed system provides a good performance with less latency.

ABSTRACT We propose the use of low‐rate layered serially concatenated low‐density generator matrix (SCLDGM) codes in interleave‐division multiple access (IDMA) systems to approach the multiuser capacity. We study the behavior of the soft interference cancellation (SIC) detector employed in IDMA systems and design the channel codes using EXtrinsic Information Transfer (EXIT) evolution, aiming at optimizing the system performance. Simulation results show that the designed codes approach the theoretical limits and outperform previous IDMA schemes based on Turbo‐Hadamard codes. Copyright © 2010 John Wiley & Sons, Ltd.

We consider discrete-time all-analog-processing joint source-channel coding, using non-linear spiral-like curves. We assume a Rayleigh channel, where the receiver may employ or not multiple antennas. Maximum Likelihood (ML) and Minimum Mean Square Error (MMSE) detection are considered. Our results show that MMSE performs much better than ML in high CSNR in single-antenna wireless systems, while diversity combining is able to significantly reduce such performance gap, therefore making the low complexity ML decoding very attractive in the case of multiple receive antennas.

Recently, the use of analog joint source-channel coding for the transmission of data samples at high rates over AWGN channels has been proposed in the literature. Simulation results have shown that this analog scheme performs close to the theoretical limits for several source distributions and transmission rates. In this paper we study the performance of such a system in comparison with optimized capacity approaching digital Bit Interleaved Coded Modulation (BICM) schemes. We show that in practical situations the analog transmission performs better than the digital scheme with a much lower encoding and decoding complexity when considering Gaussian and Laplacian source distributions.

Recently, analog joint source-channel coding has been proposed as a means of achieving near-optimum performance for high data rates with a very low complexity. However, no experimental evaluation showing the practical feasibility of this scheme has been performed to date. In this paper, we describe a software-defined radio implementation of an analog joint source-channel coded wireless transmission system. Experimental evaluation carried out in an indoor environment making use of a wireless testbed show that the performance perfectly matches that originally reported by simulations in additive white Gaussian noise channels for signal-to-noise ratio values below 20 dB.

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.

We study the ability of low-rate Serially-Concatenated Low Density Generator Matrix (SCLDGM) codes to approach theoretical limits. Although two layer SCLDGM codes approach capacity for medium rates (0.25 <; R <; 0.9), they do not perform adequately for lower rates. In this case, substantial performance improvements are obtained using three layers.

Abstract We analyze Multiple‐Input Multiple‐Output (MIMO) coded modulation systems where either Bit‐Interleaved Coded Modulation (BICM) with spatial multiplexing or concatenation of channel coding and Space‐Time Block Codes (STBCs) is used at transmission, assuming iterative Turbo‐like decoding at reception. We optimize Serially‐Concatenated Low‐Density Generator Matrix (SCLDGM) codes (a subclass of LDPC codes) for each system configuration, with the goal of assessing its ability to approach the capacity limits in either ergodic or quasi‐static channels. Our focus is on three relevant STBCs: the Orthogonal Space‐Time Block Codes (OSTBCs) for two transmit antennas (i.e., the Alamouti code), which enables optimum detection with low complexity; the Golden code, which provides a capacity increase with respect to the input constellation; and Linear Dispersion (LD) codes, which enable practical detection in asymmetrical antenna configurations (i.e., more transmit than receive antennas) for cases in which optimum detection is infeasible. We conclude that BICM without concatenation with STBCs is in general the best option, except for Alamouti‐coded 2×1 and Golden‐coded 2×2 MIMO systems. Copyright © 2009 John Wiley & Sons, Ltd.

Based on discrete density evolution (DDE), we develop closed form expressions to predict the error floor of LDGM codes. The first, rougher, approximation is obtained by assuming perfect message passing between systematic and parity bit nodes in DDE. The second, finer, expression leads to a more involved formulation. While the rougher approximation matches well to simulation results and DDE analysis for high signal to noise ratio (additive white Gaussian noise, AWGN, channel) or low crossover probability (binary symmetric channel, BSC), the finer approximation shows a good match for a wider range in the channel quality.

The Burrows-Wheeler Transform (BWT) [1] is a block sorting algorithm which has been proven to be useful in compressing text data [2]. More recently, schemes based on the BWT have been proposed for lossless data compression using LDPC [3]-[5] and Fountain [6] codes, as well as for joint source-channel coding of sources with memory [7],[8]. In this paper we propose a source-controlled Turbo coding scheme for the transmission of sources with memory over AWGN channels also based on the Burrows-Wheeler Transform. Our approach combines the BWT with a Turbo code and employs different energy allocation techniques for the encoded symbols before their transmission. Simulation results show that the performance of the designed scheme is close (within 1.5 dB) to the theoretical Shannon limit.

We investigate the performance of a discrete-time all-analog-processing joint source channel coding system for the transmission of i.i.d. Gaussian and Laplacian sources over AWGN channels. In the encoder, two samples of an i.i.d. source are mapped into a channel symbol using a space-filling curve. Different from previous work in the literature, MMSE decoding instead of ML decoding is considered, and we focus on both high and low channel SNR regions. The main contribution of this paper is to show that the proposed system presents a performance very close to the theoretical limits, even at low SNR, as long as the curve parameters are properly optimized.

Recent developments in compressed sensing have shown that if a signal can be compressed in some basis, then it can be reconstructed in such basis from a certain number of random projections. Distributed compressed sensing, where several correlated signals are compressed in a distributed manner, has also been proposed in the literature. By allowing additional distortion, successful recovery in distributed compressed sensing can be achieved even if the projections are corrupted by noise. We extend this result by showing that in addition to sparsity, it is possible to exploit prior knowledge existing in the correlation between the signals of interest to significantly improve reconstruction performance. This is done in a fashion resembling distributed coding of digital sources.

Recent developments in compressed sensing have shown that if a signal has a low Kolmogorov complexity, then it can be reconstructed from a certain number of random projections. We study the distributed coding of correlated Gaussian sources. Both intra- and inter-correlation models are considered in the source models. Decoding schemes in which it is possible to exploit the existing correlation between the signals of interest to significantly improve reconstruction performance are presented. This is done in a fashion resembling distributed coding of digital sources.

Recent developments in compressed sensing have shown that if a signal can be compressed in some basis, then it can be reconstructed in such basis from a certain number of random projections. Distributed compressed sensing, where several correlated signals are compressed in a distributed manner, has also been proposed in the literature. By allowing additional distortion, successful recovery in distributed compressed sensing can be achieved even if the projections are corrupted by noise. We extend this result by showing that in addition to sparsity, it is possible to exploit prior knowledge existing in the correlation between the signals of interest to significantly improve reconstruction performance. This is done in a fashion resembling distributed coding of digital sources.

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.

It has been recently shown that if a, signal can be compressed in some basis, then it can be reconstructed in such basis from, a certain number of random, projections. By allowing additional distortion, this holds even if the projections are corrupted by noise. We extend this result by showing that it is possible to exploit prior knowledge (e.g., if the signal is a realization of a stochastic process,) to significantly improve reconstruction performance. This is done in a fashion resembling standard joint source-channel coding of digital sources. Moreover, the exploitation of such knowledge allows for reconstruction in bases where the signal is not sparse

Nonuniform sources can be found in real world applications as uncompressed speech, text and medical images. In this paper we compare the performance of three different methods for the transmission of such sources over AWGN and Rayleigh channels. One of the methods is the classical one considering separation between source and channel coding. The two other methods are based on source-controlled channel decoding, where data is not compressed prior to transmission and redundancy is exploited at receiver. The three methods make use of turbo codes as the channel code. Simulation results show that, in some cases and in terms of bit error rate, it may be more advantageous not to compress data prior to transmission.

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.

We propose a modified algorithm for decoding of linear codes with low-density generator matrix (LDGM codes) over finite-state binary Markov channels. In order to avoid error floors, a serial concatenation of two LDGM codes is utilized. The hidden Markov model representing the channel is incorporated into the graph corresponding to the code, and the message passing algorithm is modified accordingly. The proposed scheme clearly outperforms systems in which the channel statistics are not exploited in the decoding process, allowing reliable communication at rates which are above the capacity of a memoryless channel with the same stationary bit error probability as the Markov channel. The proposed technique can be successfully applied for real wireless channels that can be modeled with hidden Markov models, such as indoor channels. In this case, the hidden Markov model representing the wireless channel can be estimated jointly with the decoding process

We investigate the ability of serially-concatenated low density generator matrix (SCLDGM) codes to approach the capacity of ergodic multiple-input multiple-output (MIMO) channels. First, we explain how to design good SCLDGM codes using extrinsic information transfer (EXIT) analysis and optimum detection. However, optimum detection becomes unfeasible in many cases of practical interest where the number of transmitting antennas and/or the number of bits per constellation symbol is large. In this case, we resort to list sphere detection (LSD), considering two versions: maximum likelihood (ML) and maximum a posteriori (MAP). Again, we use EXIT analysis to obtain good SCLDGM codes for these specific LSD versions. We provide examples of optimized rate 1/2 SCLDGM codes and show that they perform within 1 dB from the theoretical capacity limit for a large number of antenna configurations and modulation formats. Finally, we discuss how to iteratively improve channel estimation using the soft information computed during the decoding process. We also show how the complexity of channel estimation is reduced by utilizing the candidate list calculated by the LSD.

We introduce a layered scheme based on multilevel codes and random labeling of the MIMO constellation that attains constellation-constrained outage capacity in quasi-static fading channels. The proposed approach has the desirable features of using binary codes, not requiring demapper iterations and not applying constellation-expanding linear transformations in the complex field

We propose a very simple and flexible system, based on the concatenation of LDGM, repetition, and Hadamard codes, for the design of very low-rate codes. For rates around .01, the resulting performance lies within 1.5 dB of the theoretical limits in AWGN channels

In this work we study the ability of regular serially concatenated low-density generator matrix (SCLDGM) codes to approach the capacity of MIMO channels with two transmit antennas. We focus on two schemes: spatial multiplexing in a BICM fashion and concatenation with the Alamouti code. We show that good SCLDGM codes designed for SISO channels also approach MIMO capacity in the Alamouti concatenated scheme. These codes, however, do not perform adequately in spatial multiplexing and, in this case, have to be specifically designed for each antenna configuration and modulation format. Decoding complexity in spatial multiplexing is larger but the performance is in general better because the Alamouti code reduces the MIMO channel capacity when there is more than one receiving antenna

The effect of the shaping filter excess bandwidth in the performance of symbol-sampled receivers over unknown continuous-time channels is investigated. The performance loss is determined in terms of information rate, and relative to the case of perfectly synchronized matched filtering, where the continuous-time channel has to be known a priori. Numerical results are presented for the case of a raised cosine shaping filter and 2-ray and 3-ray continuous-time channel models.

This paper reviews the theory behind distributed source coding and distributed source-channel coding and surveys the progress made in code design during the last years.

Non-systematic channel codes can have a significant advantage over systematic codes when utilizing redundancy left in the data for channel decoding. However, results previously attained show that even with such codes there is a performance gap to the theoretical limits. We propose a method that combines unequal energy allocation with source controlled decoding of non-systematic turbo codes. The method benefits from both (non-systematic codes and unequal energy allocation) to improve channel decoding performance in the presence of redundancy. Simulations demonstrate superior performance to the performances attained by each of the methods separately. A significant advantage is demonstrated over using non-systematic codes with equal energy allocation. At moderate (more practical) non-uniformities, there is also a clear advantage to the new method over unequal energy allocation with systematic turbo codes

We propose a parallel concatenated coding scheme in which the constituent codes are regular systematic linear codes with low-density generator matrix (LDGM codes). The proposed system provides a simple way to reduce the error floors characteristic of single regular LDGM codes, and leads to a performance similar to that of turbo and irregular LDPC codes with less encoding/decoding complexity. Although the whole concatenated code is in fact a single irregular LDGM code, its interpretation as a parallel scheme facilitates the design, allowing the implementation of good irregular LDGM codes in a very simple manner. The system performance is studied through density evolution and validated by simulation results

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.

We propose a system to perform data compression of correlated nonbinary sources when the correlation between sources is not known, either at the encoder or the decoder. The sequences of nonbinary symbols are transformed into sequences of bits, and then source coded using punctured turbo codes, with the puncturing adjusted to achieve the desired compression rate. Each source is compressed without knowledge about the other source, and the correlation model is not assumed to be known at the encoder. The source decoder uses iterative schemes over the compressed binary sequences, and recovers the nonbinary symbol sequences from both sources. The correlation model between sources does not need to be known at the decoder, since it can be estimated jointly with the iterative decoding process. Compared with the case in which the correlation is known at the decoder, no significant performance loss is observed. The performance of the proposed scheme is close to the Slepian-Wolf theoretical limit.

The performance of symbol-sampled receivers is usually evaluated via Forney's finite impulse response (FIR) model for the equivalent channel [discrete-time transversal filter (DTTF)]. This model contains a matched filter, and, thus, requires prior knowledge of the continuous-time channel-impulse response. Therefore, if the channel is continuous and unknown, it is unrealistic to use the DTTF model, which leads to an upper bound on the system performance. Using an alternative model for the equivalent discrete-time channel, where the matched filter is replaced by a receive filter matched to the symbol waveform, we propose a framework to quantitatively investigate the performance loss from a theoretical perspective. The theoretical results are corroborated using a practical system.

We propose a modified algorithm for the decoding of linear codes with low-density generator matrix (LDGM) codes (a class of LDPC codes) over indoor and finite-state binary Markov channels. In order to avoid error floors, a concatenation of two LDGM codes is utilized. The hidden Markov model representing the channel is incorporated into the graph corresponding to the LDGM codes, and the message passing algorithm is modified accordingly. The proposed scheme clearly outperforms systems in which the channel statistics are not exploited in the decoding process, allowing reliable communication at rates which are above the capacity of a memoryless channel with the same stationary bit error probability as the Markov channel. Moreover, the proposed method can be successfully applied for real wireless channels that can be modeled with hidden Markov models, such as indoor channels.

We propose a bit interleaved coded modulation with iterative decoding (BICM-ID) scheme for MIMO systems using systematic linear codes with low-density generator matrix (LDGM). We compare the performance of the proposed scheme with the (constrained input) channel capacity and show its ability to perform close to the theoretical limit. The main advantage of the proposed structure relies on its low encoding and decoding complexity.

Symbol-sampled receivers that estimate the channel blindly or semi-blindly have had their performance evaluated via Forney's FIR model for the equivalent channel, the discrete time transversal filter (DTTF). This model, which allows maximum likelihood sequence estimation, contains a matched filter, and thus requires a priori knowledge of the continuous-time channel impulse response. As a consequence, if the channel is continuous and unknown, and if we keep sampling at the symbol-rate, it is unrealistic to use the DTTF model, which becomes an upper bound on the system performance. Using an alternative model for the equivalent discrete-time channel, where the continuous-time channel is unknown and the matched filter is replaced by a receive filter matched to the symbol waveform, we investigate the performance loss considering both a theoretical and a practical point of view. First, we estimate the theoretical performance loss, in terms of the information rate, relative to the case when the continuous-time channel is known a priori (DTTF). Later, considering a practical system for semi-blind symbol-sampled equalization and turbo decoding, we perform computer simulations which agree with the theoretical results, and show that the loss in performance is negligible when the optimum sampling instant is available.

A source-controlled turbo coding scheme for nonuniform binary memoryless sources sent over AWGN channels is designed. The use of nonbinary signaling allows performance improvements over previous schemes. The source and channel parameters can be estimated in the encoding and decoding processes.

We propose a method that allows the use of practical error correcting codes of fixed length to achieve performance close to the Slepian-Wolf boundary without the use of explicit time-sharing arguments

We propose a modified algorithm for decoding of linear codes with low-density generator matrix (LDGM codes) over finite-state binary Markov channels. Simulation results show that the proposed scheme outperforms regular LDPC codes and achieves a performance close to that of turbo codes with much less encoding/decoding complexity

We consider a source sensed by several correlated sensors. Each one of the sensors receives a noisy version of the source, performs channel coding, and transmits this information through an independent noisy channel to a central processing unit. The processing unit fuses all the received sequences with the objective of obtaining the best possible estimate of the source of interest. This is performed by utilizing at the decoder a graphical model that jointly describes the channel decoders for each sensor and the probabilistic dependence between the source and the sensors.

We present a semiblind iterative receiver for space-time coded data transmitted through unknown MIMO frequency selective quasistatic Rayleigh fading channels. The expectation maximization (EM) algorithm is used in the channel estimation process, while a BCJR algorithm for multiple antenna systems is used for both data detection, over the channel trellis, and decoding, over the space-time code trellis. Noise variance estimation is also performed by the receiver. Simulation results showing the diversity gain over MIMO flat fading channels are presented.

We propose a modified algorithm for decoding of low-density parity-check codes over finite-state binary Markov channels. The proposed approach clearly outperforms systems in which the channel statistics are not exploited in the decoding, even when the channel parameters are not known a priori at the decoder.

Summary form only given. The problem of compressing correlated binary sources when the correlation between sources is defined by a hidden Markov model (HMM) was considered. Specifically, the HMM describes the correlation pattern such as the modulo-2 addition of the two sources. A density evolution analysis of a compression system was developed using irregular LDPC codes as source codes. To achieve this goal, the standard density evolution approach was modified to incorporate the HMM. It was then applied to the design of irregular codes to optimize system performance. The key to the incorporation of HMM in density evolution is to find the input-output characteristic of the forward-backward (F-B) decoding algorithm. The output of the F-B block subtitles the a priori message in the traditional density evolution case. Theoretical results agree with the simulations and show that it is possible to achieve a performance loss close to the theoretical Slepian-Wolf limit.

Decision directed channel estimation is investigated in this paper. For systems with multiple transmit antennas, high computation complexity of the inversion of a data-dependent matrix hinders the application of optimal channel estimation. An iterative method is introduced to avoid the matrix inversion. Performance of the proposed method is demonstrated by simulation results.

A system based on the use of systematic linear codes with low-density generator matrix (LDGM codes) for joint source-channel coding of multiterminal correlated binary sources is proposed. The encoding structure and different decoding possibilities are investigated and evaluated. For the case of binary sources and AWGN channels, the resulting performance is close to the theoretical limits.

We present two decoding structures which combine turbo detection and decoding, allowing communication in the presence of intersymbol interference (ISI). The first one treats the ISI as another constituent decoder which participates in the exchange of extrinsic information, and performs slightly worse than the second structure, which combines the trellis representing each one of the constituent encoders with the ISI trellis. We show that for both methods, it is possible to obtain good performance, even when no a priori information about the ISI channel is available to the decoder.

We consider the case of two correlated non-binary sources. Data compression is achieved by transforming the sequences of non-binary symbols into sequences of bits and then using punctured turbo codes as source encoders. Each source is compressed without knowledge about the other source, and no information about the correlation between sources is required in the encoding process. Compression is achieved because of puncturing, which is adjusted to obtain the desired compression rate. The source decoder utilizes iterative schemes over the compressed binary sequences, and recovers the non-binary symbol sequences from both sources. The performance of the proposed scheme is close to the theoretical limit predicted by the Slepian-Wolf (1973) theorem.

We consider the design of a space-time coded modulation transmit-receive system employing symbol interleaving. An iterative receiver is derived based on the expectation-maximization algorithm with soft output symbol decoding and estimation of noise variance. The proposed system demonstrates very good performance over fast fading unknown channels in the presence of unknown additive white Gaussian noise.

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 models for the channels of interest. We propose a novel method, based on stochastic context-free grammars, to model channels described by long well-defined error bursts interleaved with longer error-free intervals. Compared with previous approaches based on Markov chains, the proposed model achieves a much better performance with similar training complexity. It also outperforms specific methods based on hidden Markov models developed for the same type of bursty channels, with the additional advantage of requiring much less training computation.

We describe parallel concatenated codes for communication over finite-state binary Markov channels. We present encoder design techniques and decoder processing modifications that utilize the a priori statistics of the channel and show that the resulting codes allow reliable communication at rates which are above the capacity of a memoryless channel with the same stationary bit error probability as the Markov channel. These codes outperform systems based on the traditional approach of using a channel interleaver to create a channel which is assumed to be memoryless. In addition, we introduce a joint estimation/decoding method that allows the estimation of the parameters of the hidden Markov model when they are not known a priori.

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.

We study the effect of asymmetric energy allocations to the output bits of turbo codes. We show that the error floor improves as more energy is given to the non-systematic bits. The standard (symmetric) energy allocation scheme achieves the best results in the waterfall region. However, the degradation observed in the waterfall region is practically non-existent when asymmetric energy allocation schemes close to the standard one are utilized. This implies that by using an appropriate energy allocation strategy, significant improvement in the error floor can be achieved with no practical degradation in the waterfall region.

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.

We describe techniques for joint source-channel coding of hidden Markov sources using a modified turbo decoding algorithm. This avoids the need to perform any explicit source coding prior to transmission, and instead allows the decoder to utilize the a priori structure due to the hidden Markov source. In addition, we present methods that allow the decoder to estimate the parameters of the Markov model. In combination, these techniques allow the decoder to identify, estimate, and exploit the source structure. The estimation does not degrade the performance of the system, i.e. the joint estimation/decoding allows convergence at the same noise levels as a system in which the decoder has perfect a priori knowledge of the source parameters.

We describe parallel concatenated codes for communication over binary-input, binary-output hidden Markov channels. We present encoder design techniques and decoder processing modifications that utilize the a priori statistics of the channel and show that the resulting codes allow reliable communication at rates which are above the capacity of a memoryless channel with the same stationary bit error probability as the Markov channel. These codes outperform systems based on the traditional approach of using a channel interleaver to create a channel which is assumed to be memoryless. In addition, we introduce a joint estimation/decoding method that allows the estimation of the parameters of the Markov model when they are not known a priori.

We present an analytical framework for describing the distortion in an image communication system that includes wavelet transformation, uniform scalar quantization, run length coding, entropy coding, forward error control, and transmission over a binary symmetric channel. Simulations performed using ideal source models as well as real image subbands confirm the accuracy of the distortion description. The resulting equations can be used to choose channel code rates in an unequal error protection scheme in which subbands are protected according to their importance.

We describe the methodology and results of parameter optimizations performed with the aid of analytical expressions for the channel-induced distortion in a wavelet-based image communication system. R(D) curves are calculated independently for each subband, with each point on each curve representing (in addition to rate and distortion) an optimal choice of quantizer step size and channel code rate. A global optimization procedure is then performed to identify the best operating point on the R(D) curve for each subband.

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.