Reduced Neighborhood Search Algorithms for Low Complexity Detection in MIMO Systems

Neighborhood search algorithms such as likelihood ascent search (LAS) and reactive tabu search (RTS) have been proposed for low complexity detection in multiple-input multiple-output (MIMO) systems having a large number of antennas. Both these algorithms are iterative and search for the vector which minimizes the maximum likelihood (ML) cost in the neighborhood. In this paper we propose a way to reduce the size of the neighborhood. For this, we propose a metric and a selection rule to decide whether or not to include a vector in the neighborhood. We use the indices of, say $K$, largest components of the metric for generating a reduced neighborhood set. This reduced set is used to evaluate the performance of the resulting LAS and RTS algorithms. Simulation results show that this reduces the complexity significantly while maintaining the error performance. We also show that the proposed reduced neighborhood algorithms can make MIMO systems with several hundred antenna pairs feasible.