Hierarchical Codebook based Multiuser Beam Training for Millimeter Wave Massive MIMO

TL;DR

The paper addresses the high overhead of beam training in mmWave massive MIMO by proposing a hierarchical, simultaneous multiuser beam training framework. It develops an AMCF-based codeword design for UEs under constant modulus constraints, with a fast ZCI initialization, and an adaptive hierarchical BS codebook that uses two-codeword per layer to create multi-mainlobe beams guided by prior-layer results. The approach significantly reduces training overhead while approaching the performance of full beam sweeping, as validated by simulations showing superior UE codeword design and close-to-optimal sum-rates in multiuser settings. The work offers practical gains for multiuser mmWave systems by enabling concurrent beam training with reduced feedback and overhead, with implications for scalable hybrid precoding and CSI acquisition. Future extensions include feedback reduction and wideband channel adaptations.

Abstract

In this paper, 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.

Figures (7)

• Figure 1: Illustration of a multiuser mmWave massive MIMO system with a BS and $K$ UEs.
• Figure 2: Illustration of adaptive hierarchical codebook $\boldsymbol{\mathcal{C}}$.
• Figure 3: Beam gain of different codewords in adaptive hierarchical codebook $\boldsymbol{\mathcal{C}}$ with $N_{\rm BS}=128$, $N_{\rm UE}=16$ and $K=4$.
• Figure 4: Comparisons of beam patterns using different codeword design schemes for each UE.
• Figure 5: Comparisons of beam training performance in terms of success rate using the hierarchical codebooks designed by different schemes.