Heterogeneity-agnostic AI/ML-assisted beam selection for multi-panel arrays

TL;DR

This paper introduces a heterogeneity-agnostic AI/ML-based beam selection method for multi-panel arrays that predicts propagation characteristics from user location, enabling effective beam selection across diverse antenna configurations without retraining.

Contribution

It proposes a unifying AI/ML algorithm that decouples propagation from antenna heterogeneity, allowing generalizable beam selection without multiple models or retraining.

Findings

Spectral efficiency close to genie-aided selection.

Effective across various antenna configurations.

No need for retraining for different hardware setups.

Abstract

AI/ML-based beam selection methods coupled with location information effectively reduce beam training overhead. Unfortunately, heterogeneous antenna hardware with varying dimensions, orientations, codebooks, element patterns, and polarization angles limits their feasibility and generalization. This challenge requires either a heterogeneity-agnostic model functional under these variations, or developing many models for each configuration, which is infeasible and expensive in practice. In this paper, we propose a unifying AI/ML-based beam selection algorithm supporting antenna heterogeneity by predicting wireless propagation characteristics independent of antenna configuration. We derive a reference signal received power (RSRP) model that decouples propagation characteristics from antenna configuration. We propose an optimization framework to extract propagation variables consisting of angle-of-arrival (AoA), angle-of-departure (AoD), and a matrix incorporating path gain and channel depolarization from beamformed RSRP measurements. We develop a three-stage autoregressive network to predict these variables from user location, enabling RSRP calculation and beam selection for arbitrary antenna configurations without retraining or having a separate model for each configuration. Simulation results show our heterogeneity-agnostic method provides spectral efficiency close to that of genie-aided selection both with and without antenna heterogeneity.