Channel Path Identification in mmWave Systems With Large-Scale Antenna Arrays
Ziming Cheng, Meixia Tao, Pooi‐Yuen Kam
Abstract
We consider the uplink channel estimation problem in a millimeter wave (mmWave) system with large-scale antenna arrays. Unlike many existing works which estimate the channel assuming that the number of channel paths is known a priori, we address the problem of channel estimation with an unknown number of channel paths. The spatial channel is transformed into the beamspace channel by the discrete Fourier transform (DFT). Based on the sparsity property of the beamspace channel, we propose three algorithms to estimate the number of paths, direction of arrivals (DoAs) and path gains. The first one is the Spectrum Weighted Identification of Signal Sources (SWISS) for the case when the channel statistics are unknown, which introduces a weight vector to amplify the desired signal and suppress the noise. The second one is the Neyman-Pearson criterion based-Detector (NPD) based on the Rician channel model, which adopts the Neyman-Pearson criterion to decide whether there exists a path on each DFT point. In practice, the DoAs are continuously distributed, leading to the power leakage problem. We solve this leakage problem by proposing the combined algorithm with leakage (CAL). Simulation results show that the proposed algorithms perform better than the conventional spatial smoothing.