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Rethinking Beam Management: Generalization Limits Under Hardware Heterogeneity

Nikita Zeulin, Olga Galinina, Ibrahim Kilinc, Sergey Andreev, Robert W. Heath

TL;DR

The critical need to treat hardware heterogeneity as a first-class design concern in ML-aided beam management in 5G and beyond is highlighted.

Abstract

Hardware heterogeneity across diverse user devices poses new challenges for beam-based communication in 5G and beyond. This heterogeneity limits the applicability of machine learning (ML)-based algorithms. This article highlights the critical need to treat hardware heterogeneity as a first-class design concern in ML-aided beam management. We analyze key failure modes in the presence of heterogeneity and present case studies demonstrating their performance impact. Finally, we discuss potential strategies to improve generalization in beam management.

Rethinking Beam Management: Generalization Limits Under Hardware Heterogeneity

TL;DR

The critical need to treat hardware heterogeneity as a first-class design concern in ML-aided beam management in 5G and beyond is highlighted.

Abstract

Hardware heterogeneity across diverse user devices poses new challenges for beam-based communication in 5G and beyond. This heterogeneity limits the applicability of machine learning (ML)-based algorithms. This article highlights the critical need to treat hardware heterogeneity as a first-class design concern in ML-aided beam management. We analyze key failure modes in the presence of heterogeneity and present case studies demonstrating their performance impact. Finally, we discuss potential strategies to improve generalization in beam management.
Paper Structure (22 sections, 3 figures)

This paper contains 22 sections, 3 figures.

Table of Contents

  1. Introduction
  2. Data-Driven Beam Management in 5G/B5G
  3. Beam Management Procedures in 5G NR
  4. ML Approaches for Beamforming Configuration
  5. Enhancing Model Generalization
  6. Input Feature Space and Contextual Information
  7. Target Performance Metrics
  8. Toward Heterogeneity-Aware Beam Prediction
  9. Enhancing Beam Prediction with Domain Knowledge
  10. Antenna Geometry Mismatch
  11. Impact of Codebook Design
  12. Environmental Diversity
  13. Differences in Computational Capabilities
  14. Case Study: ML-Aided Beam Alignment
  15. Modeling Setup and Algorithms
  16. ...and 7 more sections

Figures (3)

  • Figure 1: Illustration of heterogeneity types in data-driven beam management.
  • Figure 2: 3D layout of simulation scenario and SUMO snapshot.
  • Figure 3: Demonstration of performance drop under different UE heterogeneity types. The bars indicate the sample mean of the SE drop relative to the genie-based SE (thus, in %), and the 90th percentile illustrates the variation in the distribution of the relative SE values. In the "train" setup, the DNN-based beam predictor (blue) always performs on par or better than two-level HS (orange) and ES (green). Under heterogeneous "test" conditions, however, the DNN-based predictor exhibits pronounced performance degradation and high variability, particularly under codebook and location heterogeneity.