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We consider a wireless sensors network scenario where two nodes detectcorrelated sources and deliver them to a central collector via a wireless link.Differently from the Slepian-Wolf approach to distributed source coding, in theproposed scenario the sensing nodes do not perform any pre-compression of thesensed data. Original data are instead independently encoded by means oflow-complexity convolutional codes. The decoder performs joint decoding withthe aim of exploiting the inherent correlation between the transmitted sources.Complexity at the decoder is kept low thanks to the use of an iterative jointdecoding scheme, where the output of each decoder is fed to the other decoder'sinput as a-priori information. For such scheme, we derive a novel analyticalframework for evaluating an upper bound of joint-detection packet errorprobability and for deriving the optimum coding scheme. Experimental resultsconfirm the validity of the analytical framework, and show that recursive codesallow a noticeable performance gain with respect to non-recursive codingschemes. Moreover, the proposed recursive coding scheme allows to approach theideal Slepian-Wolf scheme performance in AWGN channel, and to clearlyoutperform it over fading channels on account of diversity gain due tocorrelation of information.

Design of optimal convolutional codes for joint decoding of correlated sources in wireless sensor networks