Breadth-first signal decoder: A novel maximum-likelihood scheme for multi-input multi-output systems

Abstract

A number of decoding schemes have recently been proposed to perform maximum-likelihood (ML) detection for multi-input-multi-output (MIMO) systems. In this paper, employing a "breadth-first" search algorithm for closet points in a lattice, we propose a novel ML decoding scheme called the breadth-first signal decoder (BSIDE). Through analysis and computer simulations, it is shown that the BSIDE has the same bit-error-rate performance as the conventional ML decoders while allowing significantly lower computational complexity. In addition, we introduce a simple tuning scheme that allows the BSIDE to have a performance - complexity tradeoff capability as necessary.