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Hashing Beam Training for Near-Field Communications

Yuan Xu, Li Wei, Chongwen Huang, Chen Zhu, Zhaohui Yang, Jun Yang, Jiguang He, Zhaoyang Zhang, Mérouane Debbah

TL;DR

The paper tackles efficient beam training for mmWave near-field communications by exploiting polar-domain sparsity to construct a near-field single-beam codebook and then applying random hashing to assemble a multi-arm beam codebook. A soft-decision and voting mechanism demultiplexes multi-BS superimposed signals to identify the correct beam directions, achieving up to $96.4\%$ of exhaustive training accuracy with a logarithmic overhead $Q=BL=O(B\log MN)$. The approach also demonstrates applicability to far-field conditions, preserving high identification accuracy while dramatically reducing training duration. Overall, the work offers a scalable, high-accuracy beam-training solution for multi-BS mmWave systems in both near- and far-field regimes, enabling faster deployment and improved robustness.

Abstract

In this paper, we investigate the millimeter-wave (mmWave) near-field beam training problem to find the correct beam direction. In order to address the high complexity and low identification accuracy of existing beam training techniques, we propose an efficient hashing multi-arm beam (HMB) training scheme for the near-field scenario. Specifically, we first design a set of sparse bases based on the polar domain sparsity of the near-field channel. Then, the random hash functions are chosen to construct the near-field multi-arm beam training codebook. Each multi-arm beam codeword is scanned in a time slot until all the predefined codewords are traversed. Finally, the soft decision and voting methods are applied to distinguish the signal from different base stations and obtain correctly aligned beams. Simulation results show that our proposed near-field HMB training method can reduce the beam training overhead to the logarithmic level, and achieve 96.4% identification accuracy of exhaustive beam training. Moreover, we also verify applicability under the far-field scenario.

Hashing Beam Training for Near-Field Communications

TL;DR

The paper tackles efficient beam training for mmWave near-field communications by exploiting polar-domain sparsity to construct a near-field single-beam codebook and then applying random hashing to assemble a multi-arm beam codebook. A soft-decision and voting mechanism demultiplexes multi-BS superimposed signals to identify the correct beam directions, achieving up to of exhaustive training accuracy with a logarithmic overhead . The approach also demonstrates applicability to far-field conditions, preserving high identification accuracy while dramatically reducing training duration. Overall, the work offers a scalable, high-accuracy beam-training solution for multi-BS mmWave systems in both near- and far-field regimes, enabling faster deployment and improved robustness.

Abstract

In this paper, we investigate the millimeter-wave (mmWave) near-field beam training problem to find the correct beam direction. In order to address the high complexity and low identification accuracy of existing beam training techniques, we propose an efficient hashing multi-arm beam (HMB) training scheme for the near-field scenario. Specifically, we first design a set of sparse bases based on the polar domain sparsity of the near-field channel. Then, the random hash functions are chosen to construct the near-field multi-arm beam training codebook. Each multi-arm beam codeword is scanned in a time slot until all the predefined codewords are traversed. Finally, the soft decision and voting methods are applied to distinguish the signal from different base stations and obtain correctly aligned beams. Simulation results show that our proposed near-field HMB training method can reduce the beam training overhead to the logarithmic level, and achieve 96.4% identification accuracy of exhaustive beam training. Moreover, we also verify applicability under the far-field scenario.
Paper Structure (8 sections, 26 equations, 6 figures, 1 algorithm)

This paper contains 8 sections, 26 equations, 6 figures, 1 algorithm.

Table of Contents

  1. Introduction
  2. System Model
  3. Near-Field Codebook Generation
  4. Near-Field Single-Beam Codebook
  5. Hashing Multi-Arm Beam Codebook Generation
  6. Near-Field Beam Training
  7. Simulation Results
  8. Conclusion

Figures (6)

  • Figure 1: Downlink mmWave communication scenario with $K$ BSs, and a typical user.
  • Figure 2: The schematic diagram for hashing implementation.
  • Figure 3: Success beam identification accuracy versus SNR.
  • Figure 4: Success beam identification accuracy versus SNR when considering soft and hard decisions.
  • Figure 5: Success beam identification accuracy versus SNR under the far-field simulation condition.
  • ...and 1 more figures