Sub-Array-Based Millimeter Wave Massive MIMO Channel Estimation
Xuan Zhu, Yang Liu, Cheng‐Xiang Wang
Abstract
Combination of millimeter wave (mmWave) and massive multiple input multiple output (MIMO) forms a promising technology for future sixth generation networks. As the number of antennas increases, propagation channel starts yielding spherical wavefronts, for which the traditional channel estimation algorithms are no longer applicable. To overcome this technical gap, a novel MIMO channel estimation scheme based on the assumption of spherical wavefront is proposed in this letter. The large antenna array is first divided into several sub-arrays, and then the channel estimation is performed for each sub-array based on the orthogonal matching pursuit algorithm. In addition, the joint estimation of angle of arrivals and departures (AoAs and AoDs) of each uniform planar sub-array is transformed into separate estimation by dimensionality reduction. Next, an iterative estimation is performed using Taylor expansion to continuously approximate the real grid points to recover the AoAs/AoDs. Lastly, the superiority of the proposed algorithm is verified by numerical simulations, where the proposed algorithm exhibits the best NMSE performance, compared with the existing MIMO channel estimation methods.