Joint channel decoding with feedback power control in sensor networks with correlated sources
ABSTRACT In this paper, we derive feedback power control strategies for block-faded multiple access schemes with correlated sources. In particular, upon the derivation of the feasible signal-to-noise ratio (SNR) region for the considered multiple access schemes, i.e., the multidimensional SNR region where error-free communications are, in principle, possible, two feedback power control strategies are proposed: (i) a ldquoclassicalrdquo feedback power control strategy, which aims at ldquoequalizingrdquo the SNRs at the AP over all communication links, and (ii) an ldquooptimalrdquo feedback power control strategy, which tries to make the network operational point fall in the feasible SNR region at the lowest energy consumption. These strategies will be referred to as balanced SNR and unbalanced SNR, respectively. While they require unlimited power control range at the sources (the sensors), we then propose ldquopracticalrdquo implementations by limiting the power control range. We evaluate the benefits brought by the use of the proposed feedback power control strategies considering joint channel decoding (JCD) schemes, where the sensors use channel coding and a proper iterative decoding algorithm is considered at the AP. The analysis is carried out considering the use of low-density parity-check (LDPC) codes and a serially concatenated convolutional code (SCCC).
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ABSTRACT: Parallel independent channels where no encoding is allowed for one of the channels are studied. The Slepian-Wolf theorem on source coding of correlated sources is used to show that any information source whose entropy rate is below the sum of the capacity of the coded channel and the input/output mutual information of the uncoded channel is transmissible with arbitrary reliability. The converse is also shown. Thus, coding of the side information channel is unnecessary when its mutual information is maximized by the source distribution. Applications to superposed coded/uncoded transmission on Gaussian channels are studied and an information-theoretic interpretation of Parallel-Concatenated channel codes and, in particular. Turbo codes is put forth.European Transactions on Telecommunications 08/1995; 6(5):587 - 600. · 1.05 Impact Factor
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ABSTRACT: This letter considers low-density parity-check (LDPC) coding of correlated binary sources and a novel iterative joint channel decoding without communication of any side information. We demonstrate that depending on the extent of the source correlation, additional coding gains can be obtained. Two stages of iterative decoding are employed. During global iterations, updated estimates of the source correlation are obtained and passed on to the sum-product decoder that performs local iterations with a predefined stopping criterion and/or a maximum number of local decoding iterations. Simulation results indicate that very few global iterations (2-5) are sufficient to reap significant benefits from implicit knowledge of source correlation. Finally, we provide analytical performance bounds for our iterative joint decoder and comparisons with sample simulation results.IEEE Transactions on Communications 05/2006; · 1.98 Impact Factor
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ABSTRACT: The multiple-access channel with feedback and correlated sources (MACFCS) models a sensor network in which sensors collect and transmit correlated data to a common sink. We present four achievable rate regions and a capacity outer bound for the MACFCS. For the first achievable region, we construct a decode-forward based coding strategy. The sources first exchange their data, and then cooperate to send full information to the destination. We term this strategy full decoding at sources with decode-forward (FDS-DF). For two of the other achievable regions, we first perform Slepian-Wolf coding to remove the correlation among the source data. This is followed by either (i) a compress-forward based coding strategy for the multiple-access channel with feedback, or (ii) an existing coding strategy for the multiple-access channel. We also find another achievable region using a multihop coding strategy, which only uses point-to-point coding (no cooperation). From numerical computations, we see that different strategies perform better under certain source correlation structures and network topologies. More specifically, FDS-DF approaches the capacity when (i) the inter-source distance decreases, or (ii) the correlation among the sources gets higher. Furthermore, the cooperative coding strategies considered support larger achievable rate regions than the noncooperative multihop strategy.IEEE Transactions on Information Theory 11/2007; · 2.65 Impact Factor