Sparse Matrix Precoded MIMO: A Computationally-Efficient Approach for High Throughput Wireless Links

We propose a novel sparse matrix precoding (SMP) scheme to increase the spectral efficiency (SE) of MIMO systems while leveraging the belief propagation decoding on a sparse bipartite graph at the receiver to obtain near-optimal performance with low computational complexity. Furthermore, conventional MIMO precoding/ equalization has been targeted toward channel compensation. Our proposed SMP scheme is additionally aimed at increasing the SE while lowering the MIMO receiver complexity. Unlike the conventional approach of enhancing SE in bandwidth-constrained MIMO systems by employing higher-order modulation, our proposed method achieves higher SE through sparse matrix precoding applied to a lower-order modulation at the transmitter. This approach offers a key advantage: the resulting MIMO receiver is computationally more efficient. This is because our scheme is designed to enable a message-passing algorithm (on the log-likelihood ratios (LLRs) of the lower-order modulation) on the bipartite graph representing the sparse precoding matrix, rather than directly decoding a higher-order constellation. We provide information-theoretic analysis that shows the conditions under which the SMP-MIMO provides the same channel capacity as the conventional MIMO. Several simulation results showing MIMO channel capacity and bit-error-rate (BER) provide proof of the proposed concept.