It is known that source-channel separation is sub-optimal for communicating correlated Gaussian sources over a Gaussian multiple access channel (GMAC). Considering a two-to-one GMAC which undergoes Rayleigh block-fading, we present a novel approach to practical joint source-channel coding for the scenario in which the common receiver has instantaneous CSI but only the CSI distribution is available to the individual transmitters. This approach, referred to as source-channel trellis-coded vector quantization (SC-TCVQ), simply relies on using TCVQs as fixed-rate source-channel encoders. One key issue is the optimization of the TCVQ codebooks to the mean channel signal-to-noise ratio (CSNR), and to this end, we present an analytical method to obtain the rates required for codebook design. Another key issue is the joint estimation of the sources at the receiver, for which we present a detector-estimator based on the Cartesian product of the two encoder-trellises. Simulation results show that the proposed SC-TCVQ codes, in some cases, can even beat the asymptotic performance bound for a separate source-channel code consisting of a distributed vector quantizer and capacity achieving channel codes. SC-TCVQ appears to be the best known practical code design to date for the given communication problem.

We consider the problem of sending a bivariate Gaussian source over a slow-fading multiple-access channel (MAC) with no channel state information at the transmitters. A joint source-channel coding (JSCC) scheme is constructed using hybrid digital- analog (HDA) coding techniques to minimize the average distortion over the uncertain MAC. Numerical results show that, under Rayleigh fading, the proposed HDA scheme can outperform the source-channel separation approach and the uncoded approach as the power-to-noise ratio increases. A HDA scheme based on orthogonal multiple access is designed for comparison.

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.