On joint source-channel decoding and interference cancellation in CDMA-based large-scale wireless sensor networks

Motivated by potential applications in wireless sensor networks, we consider the problem of communicating a large number of correlated analog sources over a Gaussian multiple-access channel using non-orthogonal code-division multiple-access (CDMA). We present a joint source-channel decoder which exploits the inter-source correlation for interference reduction in the CDMA channel. This decoder uses a linear minimum mean square error (MMSE) multi-user detector (MUD) in tandem with a MMSE joint source decoder for multiple sources to achieve a computational complexity that scales with the number of sources. However, iterative exchange of extrinsic information between the MUD and the joint source decoder leads to improved interference cancellation. Experimental results obtained with decoding observations from Gaussian random fields show that the proposed iterative decoder can achieve a considerable performance gain compared to a non-iterative decoder. The results also show that the iterative decoder is robust against the performance degradation due to correlated interference in a non-orthogonal CDMA channel.