000161519 001__ 161519
000161519 005__ 20181203022234.0
000161519 0247_ $$2doi$$a10.1016/j.sigpro.2012.07.007
000161519 02470 $$2ISI$$a000309849400010
000161519 037__ $$aARTICLE
000161519 245__ $$aApproximate Decoding Approaches for Network Coded Correlated Data
000161519 269__ $$a2013
000161519 260__ $$aAmsterdam$$bInstitute of Electrical and Electronics Engineers$$c2013
000161519 300__ $$a15
000161519 336__ $$aJournal Articles
000161519 520__ $$aThis paper considers a framework where data from correlated sources are transmitted with help of network coding in ad-hoc network topologies. The correlated data are encoded independently at sensors and network coding is employed in the intermediate nodes in order to improve the data delivery performance. In such settings, we focus on the problem of reconstructing the sources at decoder when perfect decoding is not possible due to losses or bandwidth bottlenecks. We first show that the source data similarity can be used at decoder to permit decoding based on a novel and simple approximate decoding scheme. We analyze the influence of the network coding parameters and in particular the size of finite coding fields on the decoding performance. We further determine the optimal field size that maximizes the expected decoding performance as a trade-off between information loss incurred by limiting the resolution of the source data and the error probability in the reconstructed data. Moreover, we show that the performance of the approximate decoding improves when the accuracy of the source model increases even with simple approximate decoding techniques. We provide illustrative examples about the possible of our algorithms that can be deployed in sensor networks and distributed imaging applications. In both cases, the experimental results confirm the validity of our analysis and demonstrate the benefits of our low complexity solution for delivery of correlated data sources.
000161519 6531_ $$aNetwork coding
000161519 6531_ $$aApproximate decoding
000161519 6531_ $$aCorrelated data
000161519 6531_ $$aDistributed transmission
000161519 6531_ $$aAd hoc networks
000161519 6531_ $$aLTS4
000161519 700__ $$aPark, Hyung Gon
000161519 700__ $$aThomos, Nikolaos
000161519 700__ $$0241061$$aFrossard, Pascal$$g101475
000161519 773__ $$j93$$k1$$q109-123$$tSignal Processing
000161519 8564_ $$uhttp://arxiv.org/pdf/1112.4210v1.pdf$$zURL
000161519 8564_ $$s1404050$$uhttps://infoscience.epfl.ch/record/161519/files/Elsevier_Signal_Processing_AD_2013_1.pdf$$yn/a$$zn/a
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000161519 917Z8 $$x101475
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000161519 937__ $$aEPFL-ARTICLE-161519
000161519 973__ $$aEPFL$$rREVIEWED$$sPUBLISHED
000161519 980__ $$aARTICLE