Multi-source cooperative networks with distributed convolutional coding

Cooperative diversity, enabled by communicators willing to collaborate, offers an effective way of mitigating slow fading propagation effects. Recently, multi-source cooperation (MSC) has been introduced to provide higher diversity and code rates relative to cooperative schemes that rely on either amplify-and-forward or regeneration of information at relay nodes. In this paper, we develop a distributed convolutionally coded (DCC) MSC system. We show that in a cooperative network with binary transmission among K active users and M idle users, the maximum diversity order is min(d(min), alpha) for any MSC scheme with code rate R and minimum (free) Hamming distance d(min), where alpha = 1 + [L(1 - R)] is the maximum possible diversity order provided by L independent Rayleigh channel gains. Notice that L = K, if cooperation takes place only between active users; and L = K + M, if M idle users also serve as relays. Compared to MSC with block coding, our DCC-MSC scheme is more effective with long codewords, when maximum likelihood decoding can be implemented using Viterbi's algorithm. We also design interleavers to maximize the diversity of the error event with minimum distance. Simulations verify that DCC-MSC can improve system performance markedly.