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Fast Beam-Brainstorm: Few-Step Generative Site-Specific Beamforming with Flexible Probing

Zihao Zhou, Zhaolin Wang, Yuanwei Liu

Abstract

A novel generative site-specific beamforming (GenSSBF) approach, termed fast beam-brainstorm (F-BBS), is proposed to address the practical bottlenecks of slow beam generation and fixed channel probing lengths in existing GenSSBF. To accelerate beam generation, F-BBS utilizes a two-stage distillation strategy that learns an average velocity field, instead of an instantaneous one, to guide the beam generative process. This strategy enables larger generation steps, realizing few-step or even one-step beam generation. Furthermore, to accommodate flexible channel probing lengths, a stochastic masking mechanism and a beam index-aware masked condition encoder are proposed, enabling a single trained model to operate with variable-length channel probing observations without retraining. Therefore, FBBS achieves the fast generation of high-fidelity communication beams from coarse and variable-length channel probing feedback, i.e., reference signal received power (RSRP), from user equipments. Simulation results on accurate ray-tracing datasets show that 1) F-BBS achieves comparable performance while reducing the beam generation cost by over 90% compared with diffusion-based GenSSBF solutions, 2) F-BBS realizes robust performance across variable channel probing length, and 3) FBBS offers a desirable trade-off between beamforming gain and beam probing overhead.

Fast Beam-Brainstorm: Few-Step Generative Site-Specific Beamforming with Flexible Probing

Abstract

A novel generative site-specific beamforming (GenSSBF) approach, termed fast beam-brainstorm (F-BBS), is proposed to address the practical bottlenecks of slow beam generation and fixed channel probing lengths in existing GenSSBF. To accelerate beam generation, F-BBS utilizes a two-stage distillation strategy that learns an average velocity field, instead of an instantaneous one, to guide the beam generative process. This strategy enables larger generation steps, realizing few-step or even one-step beam generation. Furthermore, to accommodate flexible channel probing lengths, a stochastic masking mechanism and a beam index-aware masked condition encoder are proposed, enabling a single trained model to operate with variable-length channel probing observations without retraining. Therefore, FBBS achieves the fast generation of high-fidelity communication beams from coarse and variable-length channel probing feedback, i.e., reference signal received power (RSRP), from user equipments. Simulation results on accurate ray-tracing datasets show that 1) F-BBS achieves comparable performance while reducing the beam generation cost by over 90% compared with diffusion-based GenSSBF solutions, 2) F-BBS realizes robust performance across variable channel probing length, and 3) FBBS offers a desirable trade-off between beamforming gain and beam probing overhead.
Paper Structure (23 sections, 32 equations, 12 figures, 2 tables, 2 algorithms)

This paper contains 23 sections, 32 equations, 12 figures, 2 tables, 2 algorithms.

Table of Contents

  1. Introduction
  2. System Model and Problem Formulation
  3. The Proposed Fast Beam-Brainstorm
  4. Velocity Field Learning for Beam Evolution
  5. Learning Instantaneous Velocity Field
  6. Learning Average Velocity Field
  7. Model Architecture
  8. Stochastic Masking Strategy for Variable-Length RSRP Measurements
  9. Budget-Aware Stochastic Masking
  10. Beam Index-Aware Masked Condition Encoder
  11. Training and Deployment
  12. Dataset and Simulation Results
  13. Dataset
  14. DeepMIMO I2_28B Scenario
  15. DeepMIMO O1B_28 Scenario
  16. ...and 8 more sections

Figures (12)

  • Figure 1: Comparisons of the discretization error in one-step generation, (a) learning instantaneous velocity and (b) learning average velocity.
  • Figure 2: The architecture of the 1D-DiT.
  • Figure 3: Stochastic prompt masking and the proposed beam index-aware masked condition encoder.
  • Figure 4: Illustration of the ray-tracing environments, (a) I2_28B, (b) O1B_28 and (c) HKU_28.
  • Figure 5: Beam pattern comparisons.
  • ...and 7 more figures

Theorems & Definitions (1)

  • Remark 1