Distributed Joint Source-channel Coding Research Articles

A Task-Guided Normalized Min-Sum Decoding Network for LDPC Codes-Based DJSCC

Distributed joint source-channel coding (DJSCC) refers to the correlated sources which are encoded separately and reconstructed in the centre node by joint decoding. Therefore, the challenging problem involves designing a simple and efficient decoding algorithm to utilize the correlations among distributed sources. In this letter, a multi-task deep learning decoder is proposed for the low-density parity-check (LDPC) code-based DJSCC system to improve the performance. This proposal is based on the shared neural normalized min-sum (SNNMS) decoding network and the log likelihood ratio (LLR) shared unit is added to further exploit the source correlation. Moreover, an adaptive multi-loss function is proposed to prevent the network tilting the balance in favor of one certain task. Simulation results indicate that the proposed decoder can achieve a significant performance improvement with almost the same complexity compared with the separated SNNMS decoders.

Distributed Joint Source-Channel Coding-Based Adaptive Dynamic Network Coding

Distributed Source Coding (DSC) schemes rely on separate encoding but joint decoding of statistically dependent sources, which exhibit correlation. More specifically, Distributed Joint Source-Channel coding (DJSC) is associated with the scenario, where the correlated source signals are transmitted through a noisy channel. On one hand, employing DSC or DJSC schemes exploits the existing correlation between sensors resulting in minimising the transmission energy required by the sources, while maintaining reliable communication. On the other hand, Network Coding (NC) is an efficient data transport technique leveraging network efficiency, by allowing Relay Nodes (RNs) in a communication network to combine multiple data packets received via the incoming links before transmitting them to the Destination Node (DN). In this paper, the bandwidth-efficient Distributed Joint Turbo Trellis-Coded Modulation (DJTTCM) aided by both Dynamic Network Coding (DNC) and Adaptive DNC (ADNC)-based cooperative transmission schemes are proposed. Both systems are proposed for supporting correlated source transmissions over hostile channels experiencing both small-scale and large-scale fading in which the RNs dynamically transmit its non-binary linear combinations to the DN. A substantial gain of 19.5 dB was attained at a correlation coefficient of $\rho = 0.8$ over its counterpart dispensing with NC.

Distributed Joint Source-Channel Coding Using Quasi-Uniform Systematic Polar Codes.

This paper proposes a distributed joint source-channel coding (DJSCC) scheme using polar-like codes. In the proposed scheme, each distributed source encodes source message with a quasi-uniform systematic polar code (QSPC) or a punctured QSPC, and only transmits parity bits over its independent channel. These systematic codes play the role of both source compression and error protection. For the infinite code-length, we show that the proposed scheme approaches the information-theoretical limit by the technique of joint source-channel polarization with side information. For the finite code-length, the simulation results verify that the proposed scheme outperforms the distributed separate source-channel coding (DSSCC) scheme using polar codes and the DJSCC scheme using classic systematic polar codes.

Joint Decoding and Estimation of Spatio-Temporally Correlated Binary Sources

In the context of distributed joint source-channel coding, we conceive a joint decoding and estimation scheme for binary Markov sources exhibiting spatio-temporal correlation. The proposed scheme is designed based on the serial concatenation of a trellis coded modulation (TCM) scheme and a unity-rate code. The symbol-based maximum a posteriori algorithm employed for TCM decoding is modified in order to exploit the source correlation. The estimation of both the spatial and temporal correlation parameters is performed jointly with the iterative decoding, hence allowing the estimated parameters to be updated after each iteration. Our simulation results reveal that when both the spatial and temporal correlation parameters are unknown, the proposed joint decoding and estimation scheme approaches the performance to the ideal system relying on perfectly known correlation parameters, therefore, demonstrating the superiority of the proposed scheme.

Joint Source-Channel Coding for Asymmetric Slepian-Wolf Multiple Access Relay Channel

This letter considers a code design for an asymmetric Slepian-Wolf multiple-access relay channel, in which the users and relay have different channel conditions. In this case, the conventional parity approach distributed source coding is no longer applicable. Hence, a new distributed joint source-channel superposition coding (DJSC-SC) is proposed. The proposed DJSC-SC matches the asymmetric channels, and makes full use of the good channel to obtain a better performance. Simulation results show that, compared with the conventional PA, the performance of the DJSC-SC scheme has a significant improvement and is within 1.4 dB from the capacity limit.

Robust distributed video coding for wireless multimedia sensor networks

Coding complexity and error-resilience are the two key factors for video streaming in Wireless Multimedia Sensor Networks (WMSNs). Towards this objective, this paper proposes a Robust Distributed Video Coding (RDVC) framework to optimize the quality of video transmission for WMSNs. A new error-resilient Key frame coding scheme is proposed based on the protection of Wyner-Ziv Coding (WZC), in which additional Wyner-Ziv bits play a significant role for better error resilience and better Rate/Distortion (RD) performance. In addition, following the theoretical background of Distributed Joint Source Channel Coding (DJSCC), a novel distributed source channel codec based on Group Puncture Rate Adaptive IRA code (GPRA-IRA) is proposed, upon which the error-resilient Wyner-Ziv codec is designed. These experimental results show that the proposed RDVC scheme outperforms the relevant coding in terms of RD performance, notably by up to 1-3 dB, while achieving a lower encoding complexity.

Distributed Source–Channel Coding Using Reduced-Complexity Syndrome-Based TTCM

In the context of distributed joint source–channel coding, we conceive reduced-complexity turbo trellis coded modulation (TTCM)-aided syndrome-based block decoding for estimating the cross-over probability $p_{e}$ of the binary symmetric channel, which models the correlation between a pair of sources. Our joint decoder achieves an accurate correlation estimation for varying correlation coefficients at 3 dB lower SNR, than conventional TTCM decoder, despite its considerable complexity reduction.

Distributed Source Coding and Its Applications in Relaying-Based Transmission

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.

Distributed Joint Source-Channel Coding With Copula-Function-Based Correlation Modeling for Wireless Sensors Measuring Temperature

Wireless sensor networks (WSNs) deployed for temperature monitoring in indoor environments call for systems that perform efficient compression and reliable transmission of the measurements. This is known to be a challenging problem in such deployments, as highly efficient compression mechanisms impose a high computational cost at the encoder. In this paper, we propose a new distributed joint source-channel coding (DJSCC) solution for this problem. Our design allows for efficient compression and error-resilient transmission, with low computational complexity at the sensor. A new Slepian–Wolf code construction, based on non-systematic Raptor codes, is devised that achieves good performance at short code lengths, which are appropriate for temperature monitoring applications. A key contribution of this paper is a novel Copula-function-based modeling approach that accurately expresses the correlation amongst the temperature readings from colocated sensors. Experimental results using a WSN deployment reveal that, for lossless compression, the proposed Copula-function-based model leads to a notable encoding rate reduction (of up to 17.56%) compared with the state-of-the-art model in the literature. Using the proposed model, our DJSCC system achieves significant rate savings (up to 41.81%) against a baseline system that performs arithmetic entropy encoding of the measurements. Moreover, under channel losses, the transmission rate reduction against the state-of-the-art model reaches 19.64%, which leads to energy savings between 18.68% to 24.36% with respect to the baseline system.

On Joint Source-Channel Coding for a Multivariate Gaussian on a Gaussian MAC

In this paper, nonlinear distributed joint source-channel coding (JSCC) schemes for transmission of multivariate Gaussian sources over a Gaussian multiple access channel are proposed and analyzed. The main contribution is a zero-delay JSCC named Distributed Quantizer Linear Coder (DQLC), which performs relatively close the information theoretical bounds, improves when the correlation among the sources increases, and does not level off as the signal-to-noise ratio (SNR) becomes large. Therefore it outperforms any linear solution for sufficiently large SNR. Further an extension of DQLC to an arbitrary code length named Vector Quantizer Linear Coder (VQLC) is analyzed. The VQLC closes in on the performance upper bound as the code length increases and can potentially achieve the bound for any number of independent sources. The VQLC leaves a gap to the bound whenever the sources are correlated, however. JSCC achieving the bound for arbitrary correlation has been found for the bivariate case, but that solution is significantly outperformed by the DQLC/VQLC when there is a low delay constraint. This indicates that different approaches are needed to perform close to the bounds when the code length is high and low. The VQLC/DQLC also apply for bandwidth compression of a multivariate Gaussian transmitted on point-to-point links.

Codeword averaged density evolution for distributed joint source and channel coding with decoder side information

The authors consider applying the systematic low-density parity-check codes with the parity based approach to the lossless (or near lossless) distributed joint source channel coding (DJSCC) with the decoder side information for the non-uniform sources over the asymmetric memoryless transmission channel. By using an equivalent channel coding model, which consists of two parallel subchannels: a correlation and a transmission sub-channel, respectively, they derive the codeword averaged density evolution (DE) for the DJSCC with the decoder side information for the asymmetrically correlated non-uniform sources over the asymmetric memoryless transmission channel. A new code ensemble definition of the irregular codes is introduced to distinguish between the source and the parity variable nodes, respectively. Extensive simulations demonstrate the effectiveness of the codeword averaged DE.

Convergence Analysis of Iterative Decoding for Binary CEO Problem

Estimation of a binary source using multiple observers, a variant of the so called Chief Executive Officer (CEO) problem, is considered. A low-complexity Distributed Joint Source Channel Coding (D-JSCC) based on the Parallel Concatenated Convolutional Codes (PCCC) is implemented in a cluster of sensors in a distributed fashion. Convergence of the iterative decoder is analyzed by utilizing EXtrinsic Information Transfer (EXIT) chart technique to determine the convergence region in terms of the sensors? observation accuracy and channel SNR, where the iterative decoder outperforms the non-iterative one. This leads to design of a bi-modal decoder that adaptively switches between the iterative and non-iterative modes in order to avoid inefficient iterative information exchange without compromising the resulting Bit Error Rate (BER). This adaptive decoding algorithm saves the computational power and decoding time by a factor of about 10 by avoiding unnecessary iterations.

Distributed Joint Source and Channel Coding with Low-Density Parity-Check Codes

Low-density parity-check (LDPC) codes with the parity-based approach for distributed joint source channel coding (DJSCC) with decoder side information is described in this paper. The parity-based approach is theoretical limit achievable. Different edge degree distributions are used for source variable nodes and parity variable nodes. Particularly, the codeword-averaged density evolution (CADE) is presented for asymmetrically correlated nonuniform sources over the asymmetric memoryless transmission channel. Extensive simulations show that the splitting of variable nodes can improve the coding efficiency of suboptimal codes and lower the error floor.

Distributed Joint Source-Channel Coding Using Low Density Parity Check Codes

Distributed Joint Source-Channel Coding Using Low Density Parity Check Codes

Distributed joint source-channel coding for relay systems exploiting source-relay correlation and source memory

In this article, we propose a distributed joint source-channel coding (DJSCC) technique that well exploits source-relay correlation as well as source memory structure simultaneously for transmitting binary Markov sources in a one-way relay system. The relay only extracts and forwards the source message to the destination, which implies imperfect decoding at the relay. The probability of errors occurring in the source-relay link can be regarded as source-relay correlation. The source-relay correlation can be estimated at the destination node and utilized in the iterative processing. In addition, the memory structure of the Markov source is also utilized at the destination. A modified version of the Bahl, Cocke, Jelinek, and Raviv (BCJR) algorithm is derived to exploit the memory structure of the Markov source. Extrinsic information transfer (EXIT) chart analysis is then performed to investigate convergence property of the proposed technique. Results of simulations conducted to evaluate the bit-error-rate (BER) performance and the EXIT chart analysis show that, by exploiting the source-relay correlation and source memory simultaneously, our proposed technique achieves significant performance gain, compared with the case where the correlation knowledge is not fully used.

Distributed Joint Source-Channel Coding for Correlated Sources Using Non-systematic Repeat-Accumulate Based Codes

In this paper, we propose a technique for coding the data from multiple correlated binary sources, with the aim of providing an alternative solution to the correlated source compression problem. Using non-systematic repeat-accumulate based codes, it is possible to achieve compression which is close to the Slepian---Wolf bound without relying on massive puncturing. With the technique proposed in this paper, instead of puncturing, compression is achieved by increasing check node degrees. Hence, the code rate can be more flexibly adjusted with the proposed technique in comparison with the puncturing-based schemes. Furthermore, the technique is applied to distributed joint source-channel coding (DJSCC). It is shown that in many cases tested, the proposed scheme can achieve mutual information very close to one with the lower signal-to-noise power ratio than turbo and low density generator matrix based DJSCC in additive white Gaussian noise channel. The convergence property of the system is also evaluated via the extrinsic information transfer analysis.

Binary Source Estimation Using a Two-Tiered Wireless Sensor Network

Binary source estimation using indirect observation by a cluster of wireless sensors in a two-tiered architecture is considered. The high correlation among sensors' observation is exploited in order to implement an efficient distributed Joint Source Channel Coding (DJSCC) scheme based on Parallel Concatenated Convolutional Codes (PCCC). A novel approach employing two sink nodes per cluster providing a Distributed Space-Time Block Coding (DSTBC) is proposed to get diversity gain over fading channels without need to set up an extra link between two sinks. The proposed iterative decoding algorithm is easily scalable to a large number of sensors and outperforms independent RSC decoders followed by a Maximum Likelihood (ML) detector.

Distributed Joint Source-Channel Coding in Wireless Sensor Networks

Considering the fact that sensors are energy-limited and the wireless channel conditions in wireless sensor networks, there is an urgent need for a low-complexity coding method with high compression ratio and noise-resisted features. This paper reviews the progress made in distributed joint source-channel coding which can address this issue. The main existing deployments, from the theory to practice, of distributed joint source-channel coding over the independent channels, the multiple access channels and the broadcast channels are introduced, respectively. To this end, we also present a practical scheme for compressing multiple correlated sources over the independent channels. The simulation results demonstrate the desired efficiency.

Layered Wyner–Ziv video coding for transmission over unreliable channels

Based on recent works on Wyner–Ziv coding (WZC) (or lossy source coding with decoder side information), this paper considers the case with noisy channel and addresses distributed joint source–channel coding (JSCC), while targeting at the important application of scalable video transmission over wireless networks. In WZC, after quantization, Slepian–Wolf coding (SWC) is used to reduce the rate. SWC is traditionally realized by sending syndromes of a linear channel code. Since syndromes of the channel code can only compress but cannot protect, for transmission over noisy channels, additional error protection is needed. However, instead of using one channel code for SWC and one for error protection, our idea is to use a single channel code to achieve both compression and protection. We replace the traditional syndrome-based SWC scheme by the parity-based one, where only parity bits of the Slepian–Wolf channel code are sent. If the amount of transmitted parity bits increases above the Slepian–Wolf limit, the added redundancy is exploited to cope against the noise in the transmission channel. Using irregular repeat-accumulate codes for practical parity-based SWC, we design a novel layered Wyner–Ziv video coder which is robust to channel failures and thus very suitable for wireless communications. Our simulation results show great advantages of the proposed solution based on JSCC compared to the traditional approach where source and channel coding are performed separately.