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Two-stage Multi-beam Training for Multiuser Millimeter-Wave Communications

Weijia Wang, Changsheng You, Xiaodan Shao, Rui Zhang

TL;DR

The paper tackles the challenge of efficient multiuser mmWave beam training by proposing a two-stage method that first uses sparse-subarray patterns to quickly identify candidate user angles and then employs dense-subarray patterns with cross-validation to resolve angular ambiguity. The method formalizes codebooks for both stages, derives overhead relations $T^{(I)}$ and $T^{(II)}$, and shows that with $M^{(I)}=M^{(II)}=M$, the total overhead scales as $T = \frac{N}{Q}\left(2+\frac{\log_2 Q}{4}\right)$ for $Q = M^{2}$, under the constraint $M^{3} \le N$. Numerical results at $30$ GHz demonstrate robustness and improved beam-identification rates in low-SNR regimes, while significantly reducing overhead for higher beam counts, thanks to cross-validation. The approach yields practical gains in reliability and efficiency for multiuser mmWave systems, with only one user feedback required, matching the feedback burden of single-beam exhaustive search.

Abstract

In this letter, we study an efficient multi-beam training method for multiuser millimeter-wave communication systems. Unlike the conventional single-beam training method that relies on exhaustive search, multi-beam training design faces a key challenge in balancing the trade-off between beam training overhead and success beam-identification rate, exacerbated by severe inter-beam interference. To tackle this challenge, we propose a new two-stage multi-beam training method with two distinct multi-beam patterns to enable fast and accurate user angle identification. Specifically, in the first stage, the antenna array is divided into sparse subarrays to generate multiple beams (with high array gains), for identifying candidate user angles. In the second stage, the array is redivided into dense subarrays to generate flexibly steered wide beams, for which a cross-validation method is employed to effectively resolve the remaining angular ambiguity in the first stage. Last, numerical results demonstrate that the proposed method significantly improves the success beam-identification rate compared to existing multi-beam training methods, while retaining or even reducing the required beam training overhead.

Two-stage Multi-beam Training for Multiuser Millimeter-Wave Communications

TL;DR

The paper tackles the challenge of efficient multiuser mmWave beam training by proposing a two-stage method that first uses sparse-subarray patterns to quickly identify candidate user angles and then employs dense-subarray patterns with cross-validation to resolve angular ambiguity. The method formalizes codebooks for both stages, derives overhead relations and , and shows that with , the total overhead scales as for , under the constraint . Numerical results at GHz demonstrate robustness and improved beam-identification rates in low-SNR regimes, while significantly reducing overhead for higher beam counts, thanks to cross-validation. The approach yields practical gains in reliability and efficiency for multiuser mmWave systems, with only one user feedback required, matching the feedback burden of single-beam exhaustive search.

Abstract

In this letter, we study an efficient multi-beam training method for multiuser millimeter-wave communication systems. Unlike the conventional single-beam training method that relies on exhaustive search, multi-beam training design faces a key challenge in balancing the trade-off between beam training overhead and success beam-identification rate, exacerbated by severe inter-beam interference. To tackle this challenge, we propose a new two-stage multi-beam training method with two distinct multi-beam patterns to enable fast and accurate user angle identification. Specifically, in the first stage, the antenna array is divided into sparse subarrays to generate multiple beams (with high array gains), for identifying candidate user angles. In the second stage, the array is redivided into dense subarrays to generate flexibly steered wide beams, for which a cross-validation method is employed to effectively resolve the remaining angular ambiguity in the first stage. Last, numerical results demonstrate that the proposed method significantly improves the success beam-identification rate compared to existing multi-beam training methods, while retaining or even reducing the required beam training overhead.
Paper Structure (8 sections, 1 theorem, 14 equations, 5 figures, 1 table)

This paper contains 8 sections, 1 theorem, 14 equations, 5 figures, 1 table.

Table of Contents

  1. Introduction
  2. System Model
  3. Benchmark Schemes
  4. Multi-Beam Training Based on Dense Subarrays
  5. Multi-Beam Training Based on Antenna Sparse-Activation
  6. Proposed Two-Stage Multi-Beam Training Method
  7. Numerical Results
  8. Conclusion

Key Result

Proposition 1

For the each sparse subarray $m$, by setting its steered angle as $\overline{\Omega}_{m} ,\text{ } \forall m\in\{1,2,\ldots,M^{(\rm I)}\}$, the beam codeword $\mathbf{w}^{(\rm I)}$ in (codeword1) generates a multi-beam pattern, with $Q^{(\rm I)}=(M^{(\rm I)})^{2}$ directional beams steered towards t Specifically, for each sparse subarray with $N/M^{(\rm I)}$ antennas and an inter-antenna spacing o

Figures (5)

  • Figure 1: A narrow-band far-field multiuser beam training system.
  • Figure 2: Illustration of two-stage multi-beam training.
  • Figure 3: Illustration of Example 1.
  • Figure 4: Performance comparison of the proposed two-stage multi-beam training with baselines 1-4, given different numbers of beams.
  • Figure 5: Comparison of success beam-identification rate and beam training overhead with reference $\text{SNR}=-18.3~\rm dB$ and $Q=16$.

Theorems & Definitions (1)

  • Proposition 1: Multi-beam pattern of sparse subarrays