Channel-Aware Local Search (Ca-Ls) For Iterative Mimo Detection

We propose an efficient iterative multiple-input multiple-output (MEMO) detection algorithm based on the local search. Specifically, since the MIMO channel matrix twists the lattice structure of the received symbols, the proposed channel-aware local search (CA-LS) defines its search neighborhood according to the instantaneous channel realization. Such channel-dependent neighborhood can be efficiently identified by using the sphere decoder in a set that comprises the differences between pairs of QAM vectors, which is termed as delta vectors. The delta vectors with small quadratic norms with respect to the channel matrix are identified and then used as the search directions of the CA-LS. Features like sparsity and non-uniformity of delta vectors are exploited to reduce the SD complexity. Furthermore, by reformulating the detection criterion, the log-likelihood ratio (LLR) computations and searches of the CA-LS are greatly simplified. Numerical simulations demonstrate that compared with other practical iterative MEMO detectors, e.g., the list sphere decoder, the CA-LS achieves superior performance in both error rate and complexity aspects.