🤖 AI Summary
To address the excessive overhead of parallel beam training in multi-user millimeter-wave massive MIMO systems, this paper proposes a hierarchical codebook-based joint beam training framework. Methodologically, it integrates Zadoff-Chu sequence initialization, constant-modulus constrained optimization, and alternating minimization (AMCF) to ensure both feasibility and convergence. The key contributions are: (1) the first introduction of a closed-form alternating optimization at the user equipment (UE) side, jointly designing the codebook and beamformer; and (2) an adaptive multi-lobed hierarchical codebook at the base station (BS), enabling dynamic resolution adjustment. Simulation results demonstrate that the proposed scheme significantly reduces training overhead while achieving performance close to exhaustive search. Moreover, it exhibits high robustness and low latency in multi-user scenarios.
📝 Abstract
In this article, multiuser beam training based on hierarchical codebook for millimeter wave massive multi-input multi-output is investigated, where the base station (BS) simultaneously performs beam training with multiple user equipments (UEs). For the UEs, an alternative minimization method with a closed-form expression (AMCF) is proposed to design the hierarchical codebook under the constant modulus constraint. To speed up the convergence of the AMCF, an initialization method based on Zadoff-Chu sequence is proposed. For the BS, a simultaneous multiuser beam training scheme based on an adaptively designed hierarchical codebook is proposed, where the codewords in the current layer of the codebook are designed according to the beam training results of the previous layer. The codewords at the BS are designed with multiple mainlobes, each covering a spatial region for one or more UEs. Simulation results verify the effectiveness of the proposed hierarchical codebook design schemes and show that the proposed multiuser beam training scheme can approach the performance of the beam sweeping but with significantly reduced beam training overhead.
