Motivated by potential applications in wireless sensor networks, this thesis considers 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). A joint source-channel decoder is presented which can exploit 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 (JSD) for multiple sources to achieve a computational complexity that scales with the number of sources. The MUD and the JSD, then iteratively exchange extrinsic information to improve the interference cancellation. Experimental results show that, compared to a non-iterative decoder, the proposed iterative decoder is more robust against potential performance degradation due to correlated channel interference and offers better near far resistance.

Motivated by potential applications in wireless sensor networks, this thesis considers 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). A joint source-channel decoder is presented which can exploit 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 (JSD) for multiple sources to achieve a computational complexity that scales with the number of sources. The MUD and the JSD, then iteratively exchange extrinsic information to improve the interference cancellation. Experimental results show that, compared to a non-iterative decoder, the proposed iterative decoder is more robust against potential performance degradation due to correlated channel interference and offers better near far resistance.