Search

In this paper, beam training and beam tracking are investigated for extremely large-scale multiple-input-multiple-output communication systems with partially-connected hybrid combining structures. Firstly, we propose a two-stage hybrid-field beam training scheme for both the near field and the far field. In the first stage, each subarray independently uses multiple far-field channel steering vectors to approximate near-field ones for analog combining. To find the codeword best fitting for the channel, digital combiners in the second stage are designed to combine the outputs of the analog combiners from the first stage. Then, based on the principle of stationary phase and the time-frequency duality, the expressions of subarray signals after analog combining are analytically derived and a beam refinement based on phase shifts of subarrays (BRPSS) scheme with closed-form solutions is proposed for high-resolution channel parameter estimation. Moreover, a low-complexity near-field beam tracking scheme is developed, where the kinematic model is adopted to characterize the channel variations and the extended Kalman filter is exploited for beam tracking. Simulation results verify the effectiveness of the proposed schemes.

For reconfigurable intelligent surface (RIS)-assisted mmWave massive MIMO, we propose a fast multibeam training (FMT) scheme with two stages. In the first stage, we find a multibeam together with the RIS reflection coefficients best matching the LoS links or the cascaded LoS links, where all the RISs use the same reflection coefficients at the same time to reduce the overhead of beam training. In the second stage, we find a DFT codeword best matching the LoS links or the cascaded LoS links for the BS instead of the multibeam. Different from the FMT scheme that uses a single-layer DFT codebook, in the proposed fast hierarchical multibeam training (FHMT) scheme we use a multi-layer hierarchical codebook, where in each layer only two wide beams are used to cover the angle space of interest, and the wide beams are narrowed down from top layer to bottom layer. Simulation results show that significantly smaller training overhead can be achieved by the FMT and FHMT schemes than the baseline scheme with slightly worse performance.

Non-terrestrial networks (NTNs) have become appealing resolutions for seamless coverage in the next-generation wireless transmission, where a large number of Internet of Things (IoT) devices diversely distributed can be efficiently served. The explosively growing number of IoT devices brings a new challenge for massive connection. The long-distance wireless signal propagation in NTNs leads to severe path loss and large latency, where the accurate acquisition of channel state information (CSI) is another challenge, especially for fast-moving non-terrestrial base stations (NTBSs). Moreover, the scarcity of on-board resources of NTBSs is also a challenge for resource allocation. To this end, we investigate three key issues, where the existing schemes and emerging resolutions for these three key issues have been comprehensively presented. The first issue is to enable the massive connection by designing random access to establish the wireless link and multiple access to transmit data streams. The second issue is to accurately acquire CSI in various channel conditions by channel estimation and beam training, where orthogonal time frequency space modulation and dynamic codebooks are on focus. The third issue is to efficiently allocate the wireless resources, including power allocation, spectrum sharing, beam hopping, and beamforming. At the end of this article, some future research topics are identified.