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

arXiv: https://arxiv.org/abs/2602.18678 · HTML: https://mlsanity.com/paper/2602.18678/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 11 of its 36 references that are in the map.
- First posted on arXiv 21 Feb 2026.

## Builds on (most cited first, 11 of 11)

- [CVXPY: A Python-Embedded Modeling Language for Convex Optimization](https://mlsanity.com/paper/1603.00943/) (2016)
- [Learning Radio Environments by Differentiable Ray Tracing](https://mlsanity.com/paper/2311.18558/) (2023)
- [The Integrated Sensing and Communication Revolution for 6G: Vision, Techniques, and Applications](https://mlsanity.com/paper/2405.01816/) (2024)
- [Deep Learning-based Link Configuration for Radar-aided Multiuser mmWave Vehicle-to-Infrastructure Communication](https://mlsanity.com/paper/2201.04657/) (2022)
- [Massive MIMO in 5G: How Beamforming, Codebooks, and Feedback Enable Larger Arrays](https://mlsanity.com/paper/2301.13390/) (2023)
- [A Deep Learning Approach to Location- and Orientation-aided 3D Beam Selection for mmWave Communications](https://mlsanity.com/paper/2110.06859/) (2021)
- [Harnessing Multimodal Sensing for Multi-user Beamforming in mmWave Systems](https://mlsanity.com/paper/2406.05300/) (2024)
- [Digital Twin-Assisted Explainable AI for Robust Beam Prediction in mmWave MIMO Systems](https://mlsanity.com/paper/2507.14180/) (2025)
- [Beam Management with Orientation and RSRP using Deep Learning for Beyond 5G Systems](https://mlsanity.com/paper/2202.02247/) (2022)
- [On the Mobility Analysis of UE-Side Beamforming for Multi-Panel User Equipment in 5G-Advanced](https://mlsanity.com/paper/2306.14632/) (2023)
- [Beam Training in mmWave Vehicular Systems: Machine Learning for Decoupling Beam Selection](https://mlsanity.com/paper/2404.10936/) (2024)

## Built on by (most cited first, 1 of 1)

- [RFWeave: Learning Wireless Propagation Across Scenes and Transceiver Configurations](https://mlsanity.com/paper/2610.16656/) (2026)

## Abstract (arXiv)

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.