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