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Generative Site-Specific Beamforming via Information-Maximizing Codebook

Cheng-Jie Zhao, Zhaolin Wang, Yuanwei Liu

Abstract

A novel generative site-specific beamforming (GenSSBF) framework is proposed, which integrates a site-information-maximizing (SIM) codebook with a conditional flow matching (CFM)-based beam generator. By this framework, the site-specific radio propagation environment is learned at the base station (BS), enabling the generation of high fidelity communication beams from coarse reference-signal-received-power (RSRP) feedback provided by user equipments (UEs). In the proposed design, a low-dimensional SIM probing codebook is first constructed by maximizing the mutual information between the RSRP feedback and the site-specific channel. This design not only reduces the initial beam sweeping overhead, but also enhances the amount of channel state information conveyed through UE feedback. By treating the RSRP feedback as a conditional prior, a CFM-based generative model is further developed to explicitly capture the uncertainty in beam generation. Specifically, a small set of UE-specific candidate beams is generated by inferring the learned generative model and sampling from the corresponding posterior distribution, after which the final data transmission beam is selected by the UE. Extensive simulation results demonstrate the effectiveness of both the proposed SIM codebook and the CFM-based beam generator. The proposed GenSSBF framework achieves beamforming performance nearly identical to maximum ratio transmission while requiring only eight probing beams and eight candidate beams.

Generative Site-Specific Beamforming via Information-Maximizing Codebook

Abstract

A novel generative site-specific beamforming (GenSSBF) framework is proposed, which integrates a site-information-maximizing (SIM) codebook with a conditional flow matching (CFM)-based beam generator. By this framework, the site-specific radio propagation environment is learned at the base station (BS), enabling the generation of high fidelity communication beams from coarse reference-signal-received-power (RSRP) feedback provided by user equipments (UEs). In the proposed design, a low-dimensional SIM probing codebook is first constructed by maximizing the mutual information between the RSRP feedback and the site-specific channel. This design not only reduces the initial beam sweeping overhead, but also enhances the amount of channel state information conveyed through UE feedback. By treating the RSRP feedback as a conditional prior, a CFM-based generative model is further developed to explicitly capture the uncertainty in beam generation. Specifically, a small set of UE-specific candidate beams is generated by inferring the learned generative model and sampling from the corresponding posterior distribution, after which the final data transmission beam is selected by the UE. Extensive simulation results demonstrate the effectiveness of both the proposed SIM codebook and the CFM-based beam generator. The proposed GenSSBF framework achieves beamforming performance nearly identical to maximum ratio transmission while requiring only eight probing beams and eight candidate beams.
Paper Structure (27 sections, 1 theorem, 42 equations, 10 figures, 4 tables, 3 algorithms)

This paper contains 27 sections, 1 theorem, 42 equations, 10 figures, 4 tables, 3 algorithms.

Table of Contents

  1. Introduction
  2. System Model and Problem Formulation
  3. Beam Management Procedure
  4. Signal Model
  5. RSRP Model
  6. Problem Formulation
  7. Site-Information-Maximizing Codebook
  8. Information-Theoretical Design Principles
  9. Mutual Information Maximization
  10. Orthogonality Construction
  11. Coverage Guarantee
  12. Probing Codebook Optimization
  13. Problem Reformulation
  14. Proposed Solution
  15. Convergence Analysis
  16. ...and 12 more sections

Key Result

Lemma 1

Suppose that (i) $\mathcal{U}$ contains an infinite number of i.i.d. sampled UE locations, i.e., $\left| \mathcal{U} \right| \to \infty$, and satisfies $\left\| {\bf{y}} \right\| < \infty$ for each UE; (ii) (P3) admits a KKT point within the feasible region; (iii) $\beta \to \infty$. Then, the follo

Figures (10)

  • Figure 1: Illustration for the proposed GenSSBF framework
  • Figure 2: Illustration of the proposed CFM architecture
  • Figure 3: Illustration of adopted scenarios
  • Figure 4: Convergence of the proposed codebook design method
  • Figure 5: Comparison of RSRP measurements distance between UE pairs
  • ...and 5 more figures

Theorems & Definitions (4)

  • Lemma 1
  • Remark 1: Understanding of Lemma \ref{['lemma1']}
  • Remark 2: Change of variable
  • Remark 3: Design of condition encoding