Enhanced sensing error probability estimation for iterative data fusion in the low SNR regime
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2010
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In this paper we consider a network of distributed sensors which simultaneously measure a physical parameter of interest, subject to a certain probability of sensing error. The sensed information at each of such nodes is channel-encoded and forwarded to a central receiver through parallel independent AWGN channels. In this scenario, several recent contributions have shown that the end-to-end Bit Error Rate (BER) performance can be dramatically improved if the decoders associated to each received signal and the data fusion stage exchange soft information in an iterative Turbo-like fashion. In order to achieve optimum performance, the probability of sensing error must be known (or estimated) at the receiver. In this work we describe a novel method for estimating such sensing error probability by properly weighting likelihoods output from the Soft-Input Soft-Output decoders (SISO), which is shown to outperform other estimation methods based in hard-decision comparisons, specially in the low SNR regime.
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Olabarrieta , I & Del Ser , J 2010 , Enhanced sensing error probability estimation for iterative data fusion in the low SNR regime . in 2010 International ITG Workshop on Smart Antennas, WSA 2010 . , 5456439 , 2010 International ITG Workshop on Smart Antennas, WSA 2010 , pp. 270-274 , 2010 International ITG Workshop on Smart Antennas, WSA 2010 , Bremen , Germany , 23/02/10 . https://doi.org/10.1109/WSA.2010.5456439
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