Fast Local Search Signal Detection for Massive MIMO Systems with One-Bit ADCs

We consider the problem of signal detection in a massive multiple-input multiple-output (MIMO) system with one-bit analog-to-digital converters (ADCs), where both transmitter (Tx) and receiver (Rx) are equipped with multiple antennas. Traditional maximum likelihood (ML) algorithm exhaustively searches for the optimal solution, the computational complexity of which, however, grows as the number of transmit antennas increases. In order to reduce the computational complexity, we propose a fast local search algorithm that can rapidly search for the solution with the maximum likelihood. The proposed scheme involves two steps. The first step finds an initial search point via a simple zero forcing (ZF) approach, while the second step searches for the final solution around the initial point in a local area. A curve fitting method is also adopted to further reduce the computational complexity of calculating the cumulative distribution function (CDF) of Gaussian distribution. The proposed method is fast and easy to be realized in practical systems. Simulation results show that the proposed algorithm almost achieves near ML performance for BPSK and QPSK modulation with a computational complexity reduction of more than an order of magnitude compared with existing methods.