Iterative Source-channel Decoding Research Articles

Design and Analysis of Joint Source-Channel Code System with Fixed-Length Code

As the demands of multimedia and data services increase, efficient communication systems are being investigated to meet the high data rate requirements. Joint source-channel coding (JSCC) schemes were proposed for improving overall system performance. However, existing JSCC systems may suffer a symbol error rate (SER) performance loss when residual source redundancy is not fully exploited. This paper presents a novel, low-complexity JSCC system, which consists of a fixed-length source block code and an irregular convolutional channel code. A simple approach is proposed to design source codes that minimize the SER of source detection and guarantee the convergence of iterative source-channel decoding (ISCD). To improve the waterfall performance of ISCD, the channel code is optimized by using the extrinsic information transfer (EXIT) chart and the concept of irregular code. The channel code is constituted by recursive non-systematic convolutional (RNSC) subcodes. The weights of subcodes are optimized to make the EXIT curves of the channel decoder and the source decoder well-matched, and therefore, a near-capacity performance is achieved. Simulation results show that the proposed system achieves more than 1 dB gains and 0.3 dB gains compared to the separate source-channel code system and the other optimal JSCC systems, respectively. Additionally, the performance of the proposed system is within 1 dB deviation from the Shannon limit capacity.

Iterative symbol decoding of variable-length codes with convolutional codes

In this paper, we present a symbol-level iterative source-channel decoding (ISCD) algorithm for reliable transmission of variable-length codes (VLCs). Firstly, an improved source a posteriori probability (APP) decoding approach is proposed for pack-etized variable-length encoded Markov sources. Also proposed is a recursive implementation based on a three-dimensional joint trellis for symbol decoding of binary convolutional codes. APP channel decoding on this joint trellis is realized by modification of the Bahl-Cocke-Jelinek-Raviv algorithm and adaptation to the non-stationary VLC trellis. Simulation results indicate that the proposed ISCD scheme allows to exchange between its constituent decoders the symbol-level extrinsic information and achieves high robustness against channel noises.

Iterative Symbol Decoding of Distributed Channel Encoded Fixed-Length Codes for Cooperative Communications

In this paper, we study the iterative decoding of distributed channel encoded fixed-length codes for power-efficient cooperative transmissions. By exploiting the technique of iterative source channel decoding (ISCD), the iterative decoding for cooperative transmissions exploits two types of the redundant information: the residual redundancy inherently remaining in the source codes, and the artificial redundant information provided by the distributed channel coder. To avoid the information loss on the bit-to-symbol conversion, we adopt symbol-level ISCD scheme consisting of the symbol-level channel decoder and the symbol-level source decoder. Our proposed iterative decoding can improve parameter signal-to-noise ratio (PSNR) performances by iteratively refining the extrinsic information for the source and the distributed channel decoders. Furthermore, the proposed source decoding algorithm based on the symbol-level BCJR algorithm enables the iterative decoding to efficiently exploit the extrinsic information conveyed by relay and source nodes. Simulation results show that the proposed iterative decoding scheme with the proposed source decoder and the symbol-level BCJR channel decoder delivers the robust power-efficient performance, and outperforms conventional decoding schemes.

Two-Dimensional Iterative Source-Channel Decoding for Distributed Video Coding

Motivated by the Joint Source-Channel Decoding (JSCD) principle of exploiting the source redundancy, in this treatise we study the application of iterative source-channel decoding (ISCD) conceived for distributed video coding (DVC), where the video signal is modelled by our Iterative Horizontal-Vertical Scanline Model (IHVSM) relying on a first-order Markov process. The IHVSM technique is combined with the classic forward error correction (FEC) codecs employed in the state-of-the-art DVC systems for the sake of reducing the bitrate. Our simulation results show that up to 21.5% bitrate reductions are achieved by employing the proposed ISCD technique in a DVC scheme. Alternatively, a peak signal-to-noise ratio (PSNR) gain of 2.2 dB is achieved at a bitrate of 4.5 Mbps when considering the Foreman sequence.

Iterative source and channel decoding relying on correlation modelling for wireless video transmission

Since joint source-channel decoding (JSCD) is capable of exploiting the residual redundancy in the source signals for improving the attainable error resilience, it has attracted substantial attention. Motivated by the principle of exploiting the source redundancy at the receiver, in this treatise we study the application of iterative source channel decoding (ISCD) aided video communications, where the video signal is modelled by a first-order Markov process. Firstly, we derive reduced-complexity formulas for the first-order Markov modelling (FOMM) aided source decoding. Then we propose a bit-based iterative horizontal vertical scanline model (IHVSM) aided source decoding algorithm, where a horizontal and a vertical source decoder are employed for exchanging their extrinsic information using the iterative decoding philosophy. The iterative IHVSM aided decoder is then employed in a forward error correction (FEC) encoded uncompressed video transmission scenario, where the IHVSM and the FEC decoder exchange softbit-information for performing turbo-like ISCD for the sake of improving the reconstructed video quality. Finally, we benchmark the attainable system performance against a near-lossless H.264/AVC video communication system and the existing FOMM based softbit source decoding scheme, where The financial support of the RC-UK under the auspices of the India-UK Advanced Technology Centre (IU-ATC) and that of the EU under the CONCERTO project as well as that of the European Research Council’s Advanced Fellow Grant is gratefully acknowledged. The softbit decoding is performed by a one-dimensional Markov model aided decoder. Our simulation results show that Eb=N0 improvements in excess of 2.8 dB are attainable by the proposed technique in uncompressed video applications.

Symbol-based iterative decoding of convolutionally encoded multiple descriptions

Transmission of convolutionally encoded multiple descriptions over noisy channels can benefit from the use of iterative source-channel decoding. The authors first modified the BCJR algorithm in a way that symbol a posteriori probabilities can be derived and used as extrinsic information to improve the iterative decoding between the source and channel decoders. The authors also formulate a recursive implementation for the source decoder that processes reliability information received on different channels and combines them with inter-description correlation to estimate the transmitted quantiser index. Simulation results are presented for two-channel scalar quantisation of Gauss - Markov sources which demonstrate the error-resilience capabilities of symbol-based iterative decoding.

Robust Transmission of Compressed HTML Files over Wireless Channel using an Iterative Joint Source-Channel Decoding Receiver

This paper proposes an algorithm for the robust reception of compressed HTML files transmitted over a noisy mobile radio channel. Both source encoders and transmission systems are assumed to be standard compliant. The source encoder follows the HTTP1.1 protocol specifications, i.e. the HTML files are encoded by the deflate algorithm, a combination of Lempel-Ziv and Huffman algorithms. The transmission scheme follows IEEE 802.11a (and 802.11n) standard as an example. The proposed receiver is based on an iterative joint source-channel decoding approach. The Soft-Input Soft-Output outer source decoder is based on a sequential M-algorithm, which has been modified to improve the decoding performance by exploiting the specific grammatical and syntax rules of (i) Huffman codes; (ii) Lempel-Ziv codes; and (iii) HTML language. Simulation results following the IEEE 802.11a (and 802.11n) standard over additive white Gaussian noise and Rayleigh fading channels show that the proposed receiver drastically reduces the number of errors occurring in the received HTML files compared to the classical receivers. An EXIT chart analysis illustrates some properties of this combination of source and channel decoders.

Iterative Source–Channel Decoding With Reduced Error Floors

Audio-visual source encoders for digital mobile communications extract parameters that-due to delay and complexity constraints-exhibit some residual redundancy. This residual redundancy can be exploited by iterative source-channel decoding (ISCD) to improve the robustness against transmission noise by performing soft parameter detection as part of the decoding process. Systems employing ISCD at the receiving end often exhibit an observable error floor. While this error floor can be tolerated in some cases, it is often desirable to perfectly reconstruct the source codec parameters. In this paper, we explain the reasons for the error floor and propose two solutions for realizing ISCD systems with optimized error floor performance while maintaining the desired near-capacity waterfall behavior. All approaches aim at optimizing the distance properties of the (redundant) mapping of bit patterns to the source codec parameters. In some cases, especially if small quantizer code books are employed, good mappings cannot be found-in this case, the novel multi-dimensional bit mapping allows to reduce the error floor after decoding.

EXIT Chart Based System Design for Iterative Source-Channel Decoding with Fixed-Length Codes

Audio-visual source encoders for digital wireless communications extract parameter sets on a frame-by-frame basis. Due to delay and complexity constraints these parameters exhibit some residual redundancy which manifests itself in non-uniform parameter distributions and intra- as well as inter-frame correlation. This residual redundancy can be exploited by iterative source-channel decoding (ISCD) to improve the robustness against impairments from the channel. In the design process of ISCD systems the well known EXIT charts play a key role. However, in case of inter-frame parameter correlation, the classic EXIT charts do not provide reliable bounds for predicting the convergence behavior of ISCD. We explain the reasons for the so-called overshooting effect and propose a novel extension to the EXIT chart computation which provide significantly better bounds for the decoding trajectories. Four advanced ISCD system configurations are proposed and investigated using the benefits of the improved EXIT chart based system design. These configurations include regular and irregular redundant index assignments. In addition, we incorporate unequal error protection in the optimization of irregular index assignments. We show how to realize a versatile multi-mode ISCD scheme which operates close to the theoretical limit.

Iterative Source-Channel Decoding Using Symbol-Level Extrinsic Information

Transmission of convolutionally encoded source-codec parameters over noisy channels can benefit from the turbo principle through iterative source-channel decoding. We first formulate a recursive implementation based on sectionalized code trellises for MAP symbol decoding of binary convolutional codes. Performance is further enhanced by the use of an interpolative softbit source decoder that takes into account the channel outputs within an interleaving block. Simulation results indicate that our proposed scheme allows to exchange between its constituent decoders the symbol-level extrinsic information and achieves high robustness against channel noises.

On redundant index assignments for iterative source-channel decoding

Iterative source-channel decoding (ISCD) exploits the residual redundancy of source codec parameters by using the Turbo principle. A powerful improvement for ISCD is the concept of redundant index assignments. In this letter we present new design and optimization guidelines for redundant index assignments. For block code based redundant index assignments, weak block codes such as a repetition code should be preferred. Simulation results show significant performance improvements.

Analysis of Mismatched First Order A Priori Information in Iterative Source-Channel Decoding

Due to complexity and delay constraints a usually significant amount of residual redundancy remains in the source samples after source coding. This residual redundancy can be exploited by iterative source-channel decoding for error concealment and quality improvements. One key design issue in joint source-channel (de-)coding is the index assignment. Besides conventional index assignments optimized index assignments have been developed, e.g., considering zeroth or first order a priori information of the source samples. However, in real-world scenarios it is unlikely that the amount of residual redundancy is constant over time and thus it may occur that the just deployed index assignment is suboptimal at times when the residual redundancy differs too much from the amount that it is optimized for. In this paper the performance of optimized index assignments is examined that consider first order a priori knowledge under such suboptimal conditions.

Minimum Terms of Residual Redundancy for Successful Iterative Source-Channel Decoding

Iterative source-channel decoding (ISCD) improves the error robustness of a digital communication system by iteratively evaluating natural residual source redundancy and artificial channel coding redundancy in a TURBO-like process. Based on recent results to extrinsic information transfer (EXIT) charts we present a novel (experimental) approach to quantify the minimum terms of residual redundancy which are needed for (almost) successful ISCD. Moreover, we clarify why in certain situations the decoding trajectory exceeds the EXIT-characteristic of soft decision source decoding (SDSD) in an ISCD scheme

Iterative Source-Channel Decoding With Markov Random Field Source Models

We propose a joint source-channel decoding approach for multidimensional correlated source signals. A Markov random field (MRF) source model is used which exemplarily considers the residual spatial correlations in an image signal after source encoding. Furthermore, the MRF parameters are selected via an analysis based on extrinsic information transfer charts. Due to the link between MRFs and the Gibbs distribution, the resulting soft-input soft-output (SISO) source decoder can be implemented with very low complexity. We prove that the inclusion of a high-rate block code after the quantization stage allows the MRF-based decoder to yield the maximum average extrinsic information. When channel codes are used for additional error protection the MRF-based SISO source decoder can be used as the outer constituent decoder in an iterative source-channel decoding scheme. Considering an example of a simple image transmission system we show that iterative decoding can be successfully employed for recovering the image data, especially when the channel is heavily corrupted

A turbo-coded multiple-description system for multiple antennas

We propose a joint source-channel coding scheme for wireless communication systems with multiple transmit and receive antennas. The source coder is realized by a multiple description encoder that generates multiple bit streams. Each description is then separately turbo coded and transmitted using multiple antennas. For the receiver, we describe a suitable iterative joint source-channel decoding technique that exploits the correlations between the descriptions. We present several examples that illustrate the performance of the proposed system, and compare it with other approaches.

Low-Complexity Iterative Joint Source- Channel Decoding for Variable-Length Encoded Markov Sources

In this paper, we present a novel packetized bit-level decoding algorithm for variable-length encoded Markov sources, which calculates reliability information for the decoded bits in the form of a posteriori probabilities (APPs). An interesting feature of the proposed approach is that symbol-based source statistics in the form of the transition probabilities of the Markov source are exploited as a priori information on a bit-level trellis. This method is especially well-suited for long input blocks, since in contrast to other symbol-based APP decoding approaches, the number of trellis states does not depend on the packet length. When additionally the variable-length encoded source data is protected by channel codes, an iterative source-channel decoding scheme can be obtained in the same way as for serially concatenated codes. Furthermore, based on an analysis of the iterative decoder via extrinsic information transfer charts, it can be shown that by using reversible variable-length codes with a free distance of two, in combination with rate-1 channel codes and residual source redundancy, a reliable transmission is possible even for highly corrupted channels. This justifies a new source-channel encoding technique where explicit redundancy for error protection is only added in the source encoder.

Turbo decodulation: iterative combined demodulation and source-channel decoding

We propose the combination of iterative demodulation and iterative source-channel decoding as a multiple turbo process. The receiver structures of bit-interleaved coded modulation with iterative decoding (BICM-ID), iterative source-channel decoding (ISCD), and iterative source coded modulation (ISCM) are merged to one novel turbo system, in which in two iterative loops reliability information is exchanged between the three single components, demodulator, channel decoder and (softbit) source decoder. Simulations show quality improvements compared to the different previously known systems, which use iterative processing only for two components of the receiver.

Iterative Source-Channel Decoding: Improved System Design Using EXIT Charts

The error robustness of digital communication systems using source and channel coding can be improved by iterative source-channel decoding (ISCD). The turbo-like evaluation of natural residual source redundancy and of artificial channel coding redundancy makes step-wise quality gains possible by several iterations. The maximum number of profitable iterations is predictable by an EXIT chart analysis. In this contribution, we exploit the EXIT chart representation to improve the error correcting/concealing capabilities of ISCD schemes. We propose new design guidelines to select appropriate bit mappings and to design the channel coding component. A parametric source coding scheme with some residual redundancy is assumed. Applying both innovations, the new EXIT-optimized index assignment as well as the appropriately designed recursive nonsystematic convolutional (RNSC) code allow to outperform known approaches to ISCD by far in the most relevant channel conditions.

Iterative joint source-channel decoding of variable-length codes using residual source redundancy

We present a novel symbol-based soft-input a posteriori probability (APP) decoder for packetized variable-length encoded source indexes transmitted over wireless channels where the residual redundancy after source encoding is exploited for error protection. In combination with a mean-square or maximum APP estimation of the reconstructed source data, the whole decoding process is close to optimal. Furthermore, solutions for the proposed APP decoder with reduced complexity are discussed and compared to the near-optimal solution. When, in addition, channel codes are employed for protecting the variable-length encoded data, an iterative source-channel decoder can be obtained in the same way as for serially concatenated codes, where the proposed APP source decoder then represents one of the two constituent decoders. The simulation results show that this iterative decoding technique leads to substantial error protection for variable-length encoded correlated source signals, especially, when they are transmitted over highly corrupted channels.

Performance Analysis and Design Criteria for Finite-Alphabet Source-Channel Codes

Efficient compression of finite-alphabet sources requires variable-length codes (VLCs). However, in the presence of noisy channels, error propagation in the decoding of VLCs severely degrades performance. To address this problem, redundant entropy codes and iterative source-channel decoding have been suggested, but to date, neither performance bounds nor design criteria for the composite system have been available. We calculate performance bounds for the source-channel system by generalizing techniques originally developed for serial concatenated convolutional codes. Using this analysis, we demonstrate the role of a recursive structure for the inner code and the distance properties of the outer code. We use density evolution to study the convergence of our decoders. Finally, we pose the question: Under a fixed rate and complexity constraint, when should we use source-channel decoding (as opposed to separable decoding)? We offer answers in several specific cases. For our analysis and design rules, we use union bounds that are technically valid only above the cutoff rate, but interestingly, the codes designed with union-bound criteria perform well even in low signal-to-noise ratio regions, as shown by our simulations as well as previous works on concatenated codes.