Low-Complexity Breadth-First Search Detection for Large-Scale MIMO Systems

Thanks to its near-optimal performance, breadth-first search detection (BFSD) finds widespread application in small-scale MIMO systems. However, existing BFSD methods struggle to effectively configure the width (number of candidate nodes) for each layer, resulting in prohibitive complexity in large-scale MIMO systems. To address this, we propose two width optimization schemes for BFSD. We introduce a layer-by-layer optimization framework to reduce the design space of width configurations, and a Monte Carlo-assisted method to link width configurations to detection performance. Using this linking scheme in the reduced design space, we formulate the first width optimization scheme given specific performance constraints. Then, we present another scheme that employs a theoretical linking method as an alternative to the Monte Carlo approach. Although slightly less effective, the second scheme has negligible complexity for width optimization, making it well-suited for communication scenarios with time-varying characteristics. In $128\times 128$ MIMO systems, numerical results demonstrate that the optimized BFSD using our first and second schemes can reduce complexity by up to 82% and 65%, respectively, while achieving superior detection performance compared to state-of-the-art BFSD.