Hybrid beamforming with discrete phase shifters for millimeter wave backhaul networks

Hybrid beamforming (HBF) technology becomes one of the key technologies in the millimeter wave (mmWave) mobile backhaul systems, for its lower complexity and low power consumption compared to full digital beamforming (DBF). Two structures of HBF exist in the mmWave mobile backhaul system, namely, the fully connected structures (FCS) and partially connected structures (PCS). However, the existing methods cannot be applied to both structures. Moreover, the ideal phase shifter is considered in some current HBF methods, which is not realistic. In this paper, a HBF algorithm for both structures based on the discrete phase shifters is proposed in the mmWave mobile backhaul systems. By using the principle of alternating minimization, the optimization problem of HBF is decomposed into a DBF optimization problem and an analog beamforming (ABF) optimization problem. Then the least square (LS) method is enabled to solve the optimization model of DBF. In addition, the achievable data rate for both structures with closed-form expression which can be used to convert the optimization model into a single-stream beamforming optimization model with per antenna power constraint is derived. Therefore, the ABF is easily solved. Simulation results show that the performance of the proposed HBF method can approach the full DBF by using a lower resolution phase shifter.     

基于信道模糊关联识别的NLOS测距误差补偿算法

针对NLOS测距存在误差的问题,提出一种基于信道模糊关联识别的NLOS测距误差补偿算法.根据先验信道特征参数分布信息构造信道特征参数模糊隶属矩阵,并利用灰色关联分析方法计算归一化权值矩阵,进而获得模糊综合评价矩阵来对信道环境进行识别.在此基础上,根据信道识别结果构建Huber代价函数,通过Huber线性回归方法对原始测距结果进行迭代重构,将重构结果作为Kalman滤波的测量值进行滤波.仿真结果表明,所提算法可以有效提高NLOS环境下的测距精度,在信噪比大于-2 dB时可以达到厘米级测距精度.