Abstract

Distributed source coding (DSC) schemes rely on separate encoding but joint decoding of statistically dependent sources, which exhibit correlation. DSC has numerous promising applications ranging from reduced-complexity handheld video communications to onboard hyperspectral image coding under computational limitations. The concept of separate encoding at the first sight compromises the attainable encoding performance. However, the DSC theory proves that independent encoding can in fact be designed as efficiently as joint encoding, as long as joint decoding is allowed. More specifically, distributed joint source-channel coding (DJSC) is associated with the scenario, where the correlated source signals are transmitted through a noisy channel. In this paper, we present a concise historic background of DSC concerning both its theory and its practical aspects. In addition, a series of turbo trellis-coded modulation (TTCM)-aided DJSC-based cooperative transmission schemes are proposed. DJSC scheme is conceived for the transmission of a pair of correlated sources to a destination node (DN). The first source sequence is TTCM encoded, and then, it is compressed before it is transmitted both over a Rayleigh fading channel, where the second source signal is assumed to be perfectly decoded side-information at the DN for the sake of improving the achievable decoding performance of the first source. The proposed scheme is capable of performing reliable communications for various levels of correlation near to the theoretical Slepian–Wolf/Shannon (SW/S) limit. Pursuing our objective of designing practical DJSC schemes, we further extended the above-mentioned arrangement to a more realistic cooperative communication system, where the pair of correlated sources are transmitted to a DN with the aid of a relay node (RN). Explicitly, the pair of correlated source sequences are TTCM encoded and compressed before transmission over a Rayleigh fading multiple access channel, where our proposed scheme is capable of operating within 0.55 dB from the SW/S limit for a correlation coefficient value of $\rho = 0.8$ , and within 0.07 bits of the minimum SW compression rate. The RN transmits both users’ signal with the aid of a powerful superposition modulation technique that judiciously allocates the transmit power between the two signals. The correlation is beneficially exploited at both the RN and the DN using our powerful iterative joint decoder, which is optimized using extrinsic information transfer characteristics charts.

Highlights

  • The classic Turbo Trellis-Coded Modulation (TTCM) design was outlined in [107], based on the search for the best TrellisCoded Modulation (TCM) component codes using the so-called ‘punctured’ minimal distance criterion, in order to approach the capacity of the Additive White Gaussian Noise (AWGN) channel

  • The bandwidth-efficient TTCM concept is incorporated into our distributed joint sourcechannel coding (DJSC) design, resulting in the Distributed Joint Turbo-Trellis Coded Modulation (DJSTTCM) concept conceived for transmission over a Rayleigh fading channel

  • DJSTTCM scheme [49] considered for transmitting correlated sources is illustrated in Fig. 15, where L(·) denotes the Logarithmic-Likelihood Ratios (LLR) of the bits

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Summary

INTRODUCTION

Let us commence by considering the stereographic images of the Sun in Fig. 1, which were captured using a pair of satellites that are part of NASA’s Solar Terrestrial Relations Observatory (STEREO) project. The location of. Exploiting the correlation between the images by jointly decoding them has lead to a significant power reduction, while maintaining reliable communication This room for improvement can be utilised for further source sequence compression, as it is going to be illustrated in the subsequent sections. DISTRIBUTED SOURCE CODING DSC refers to the problem of compressing several physically separated, but correlated sources, which are unable to communicate with each other by exploiting that the receiver can perform joint decoding of the encoded signals [3], [4], [7]–[9].

SLEPIAN-WOLF THEORY
WYNER-ZIV THEORY
CORRELATION MODEL
SYSTEM MODEL
JOINT SOURCE-TTCM DECODER
CONCLUSIONS
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