Abstract
Linear minimum mean square error(MMSE)detection has been shown to achieve near-optimal performance for mas-sive multiple-input multiple-output(MIMO)systems but inevitably involves complicated matrix inversion,which entails high complexity.To avoid the exact matrix inversion,a consider-able number of implicit and explicit approximate matrix inver-sion 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 detec-tion for massive MIMO uplink systems.The proposed improved Newton iteration significantly reduces the complexity of conven-tional 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 signifi-cantly lower complexity than higher Newton iterations.Conver-gence 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.
基金项目
国家自然科学基金(6237122562371227)
NETL
NSTL
万方数据