Distributed Joint Source-Channel Decoding for Correlated Markov Sources (original) (raw)
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Joint distributed source-channel decoding for LDPC-coded binary Markov sources
2013 IEEE 24th Annual International Symposium on Personal, Indoor, and Mobile Radio Communications (PIMRC), 2013
We propose a novel joint decoding technique for distributed source-channel (DSC) coded systems for transmission of correlated binary Markov sources over additive white Gaussian noise (AWGN) channels. In the proposed scheme, relatively shortlength, low-density parity-check (LDPC) codes are independently used to encode the bit sequences of each source. To reconstruct the original bit sequence, a joint source-channel decoding (JSCD) technique is proposed which exploits the knowledge of both temporal and source correlations. The JSCD technique is composed of two stages, which are iteratively performed. First, a sum-product (SP) decoder is serially concatenated with a BCJR decoder, where the knowledge of source memory is utilized during local (horizontal) iterations. Then, the estimate of correlation between the sources is used to update the concatenated decoder during global (vertical) iterations. Therefore, the correlation of the sources is assumed as side information in the subsequent global iteration of each concatenated decoder. From the simulation results of frame/bit error rate (FER/BER), we note that significant gains are achieved by the proposed decoding scheme with respect to the case where the correlation knowledge is not completely utilized at the decoder.
2008
In this paper, we give a distributed joint source channel coding scheme for arbitrary correlated sources for arbitrary point in the Slepian-Wolf rate region, and arbitrary link capacities using LDPC codes. We consider the Slepian-Wolf setting of two sources and one destination, with one of the sources derived from the other source by some correlation model known at the decoder. Distributed encoding and separate decoding is used for the two sources. We also give a distributed source coding scheme when the source correlation has memory to achieve any point in the Slepian-Wolf rate achievable region. In this setting, we perform separate encoding but joint decoding.
On the joint source-channel decoding of variable-length encoded sources: The additive-Markov case
IEEE Transactions on Communications, 2003
This paper proposes an optimal maximum a posteriori probability decoder for variable-length encoded sources over binary symmetric channels that uses a novel state-space to deal with the problem of variable-length source codes in the decoder. This sequential, finite-delay, joint source-channel decoder delivers substantial improvements over the conventional decoder and also over a system that uses a standard forward error correcting code operating at the same over all bit rates. This decoder is also robust to inaccuracies in the estimation of channel statistics.
Distributed joint source channel coding on a multiple access channel with side information
2008 IEEE International Symposium on Information Theory, 2008
We consider the problem of transmission of several distributed correlated sources over a multiple access channel (MAC) with side information at the sources and the decoder. Source-channel separation does not hold for this channel. Sufficient conditions are provided for transmission of sources with a given distortion. The source and/or the channel could have continuous alphabets (thus Gaussian sources and Gaussian MACs are special cases). Various previous results are obtained as special cases. We also provide several good joint source-channel coding schemes for discrete sources and discrete/continuous alphabet channel.
Wireless Personal Communications, 2013
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.
Serially concatenated joint source-channel coding for binary Markov sources
2011
Abstract In this paper, we propose a joint design of serially concatenated source channel coding for binary Markov sources over AWGN channels. To exploit the memory structure inherent within the sequence output from the source, modifications are made on the BCJR algorithm. To decode the outer code, the modified version of the BCJR algorithm is used, while the inner code by the standard version of the algorithm.
EURASIP Journal on Wireless Communications and Networking, 2005
We consider the case of two correlated sources, S 1 and S 2 . The correlation between them has memory, and it is modelled by a hidden Markov chain. The paper studies the problem of reliable communication of the information sent by the source S 1 over an additive white Gaussian noise (AWGN) channel when the output of the other source S 2 is available as side information at the receiver. We assume that the receiver has no a priori knowledge of the correlation statistics between the sources. In particular, we propose the use of a turbo code for joint source-channel coding of the source S 1 . The joint decoder uses an iterative scheme where the unknown parameters of the correlation model are estimated jointly within the decoding process. It is shown that reliable communication is possible at signal-to-noise ratios close to the theoretical limits set by the combination of Shannon and Slepian-Wolf theorems.
We consider the problem of distributed joint source-channel coding of correlated Gaussian sources over a Gaussian Multiple Access Channel (GMAC). There may be side information at the decoder and/or at the encoders. First we specialize a general result in [20] to obtain sufficient conditions for reliable transmission over a Gaussian MAC. This system does not satisfy the source-channel separation. We study and compare three joint source-channel coding schemes available in literature. We show that each of these schemes is optimal under different scenarios. One of the schemes, Amplify and Forward (AF) which simplifies the design of encoders and the decoder, is optimal at low SNR but not at high SNR. Another scheme is asymptotically optimal at high SNR. The third coding scheme is optimal for orthogonal Gaussian channels. We also show that AF is close to the optimal scheme for orthogonal channels even at high SNR.
Joint source-channel decoding for LDPC-coded error-corrupted binary Markov sources
2016 International Conference on Computing, Networking and Communications (ICNC), 2016
We consider the problem of joint decoding and data fusion in data gathering for densely deployed sensor networks modeled by the Chief Executive Officer (CEO) problem. More specifically, we consider the binary CEO problem where all sensors observe the same time-correlated binary Markov source corrupted by independent binary noises. Hence, the observations are two-dimensionally (temporary and spatially) correlated. In the proposed scheme, every sensor apply a low-density paritycheck (LDPC) code and transmit the corresponding codeword independently over additive white Gaussian noise (AWGN) channels. To reconstruct the original bit sequence, an iterative joint source-channel decoding (JSCD) technique is considered. To exploit the knowledge about the source correlations, we consider an iterative decoding between a sum-product (SP) decoder serially concatenated with BCJR decoder which is applied for every sensor as local iterations. Then, correlation between sensors' data is employed to update extrinsic information received from the SP-BCJR decoders of the different sensors during global iterations. We illustrate the performance of the joint decoder for different correlation setups and with different number of sensors. Simulation results, in terms of bit error rate show promising improvements compared with the separate decoding scheme where the correlation knowledge is not completely utilized in the decoder.
GLOBECOM 2009 - 2009 IEEE Global Telecommunications Conference, 2009
In this paper we address the problem of transmission of correlated sources over a fast fading multiple access channel (MAC) with partial channel state information available at both the encoders and the decoder. We provide sufficient conditions for transmission with given distortions. Next these conditions are specialized to a Gaussian MAC (GMAC). We provide the optimal power allocation strategy and compare the strategy with various levels of channel state information.