Low-Complexity Signal Detection for Massive MIMO Systems via Trace Iterative Method

Linear minimum mean square error (MMSE) detection has been shown to achieve near-optimal performance for massive multiple-input multiple-output (MIMO) systems but inevitably involves complicated matrix inversion, which entails high complexity. To avoid the exact matrix inversion, a considerable number of implicit and explicit approximate matrix inversion based detection methods is proposed. By combining the advantages of both the explicit and the implicit matrix inversion, this paper introduces a new low-complexity signal detection algorithm. Firstly, the relationship between implicit and explicit techniques is analyzed. Then, an enhanced Newton iteration method is introduced to realize an approximate MMSE detection for massive MIMO uplink systems. The proposed improved Newton iteration significantly reduces the complexity of conventional Newton iteration. However, its complexity is still high for higher iterations. Thus, it is applied only for first two iterations. For subsequent iterations, we propose a novel trace iterative method (TIM) based low-complexity algorithm, which has significantly lower complexity than higher Newton iterations. Convergence guarantees of the proposed detector are also provided. Numerical simulations verify that the proposed detector exhibits significant performance enhancement over recently reported iterative detectors and achieves close-to-MMSE performance while retaining the low-complexity advantage for systems with hundreds of antennas.