How Much Training Is Needed for Iterative Multiuser Detection and Decoding?

This paper studies large randomly spread direct-sequence code-division multiple-access system operating over a block fading multipath channel. Channel knowledge is obtained by a linear estimator whose initial decisions are iteratively refined by using a soft feedback from the single-user decoders. In addition to the traditional training symbol based signaling scheme, we study a novel method that utilizes a random bias in the symbol probabilities of the transmitted signal to construct the initial channel estimates. The numerical results suggest that in the large system limit, appropriate selection of the channel code and signaling method allows for successful communication with vanishing training overhead in overloaded systems if iterative channel and data estimation is performed at the receiver.