Heterogeneity-agnostic AI/ML-assisted beam selection for multi-panel arrays
This addresses a practical problem in wireless communication systems by reducing the need for multiple models, though it appears incremental as it builds on existing AI/ML beam selection approaches.
The paper tackles the challenge of AI/ML-based beam selection in multi-panel arrays with heterogeneous antenna hardware, proposing a method that predicts propagation characteristics independent of configuration to enable beam selection without retraining for each setup, achieving spectral efficiency close to genie-aided selection in simulations.
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.