Beyond fixed neighborhood search in the likelihood ascent algorithm for MIMO systems

Neighborhood search algorithms have been proposed for detection in large/massive multiple-input multiple-output (MIMO) systems. They iteratively search for the best vector in a fixed neighborhood. However, the ML solution may not lie in the searched space or the search may take a large number of intermediate vectors to converge. Instead of searching in a fixed neighborhood, a better way will be to look for an update which is not restricted to be in a fixed neighborhood. Motivated by this, we formulate an optimization problem to maximize the reduction in ML cost and use it to derive an expression for updating the solution. We use a metric based on the channel matrix and the error vector to determine the likelihood of a symbol being in error. Using this likelihood and the update, we propose a likelihood ascent search (LAS) algorithm to find an update which is not restricted to be in a fixed neighborhood and seeks to provide maximum reduction in ML cost. This process continues till there is a reduction in the ML cost. Compared to existing LAS based algorithms, it is found to provide better error performance, that too at a lower complexity.