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[2602.18678] Heterogeneity-agnostic AI/ML-assisted beam selection for multi-panel arrays
Summary
This paper presents a novel AI/ML-based beam selection algorithm that addresses the challenges posed by heterogeneous antenna configurations in multi-panel arrays, enhancing spectral efficiency without the need for multiple models.
Why It Matters
The research is significant as it tackles the limitations of existing beam selection methods in wireless communication, particularly in environments with diverse antenna hardware. By proposing a heterogeneity-agnostic approach, it opens avenues for more efficient and scalable wireless systems, which is crucial for the growing demand for robust connectivity.
Key Takeaways
- Introduces a heterogeneity-agnostic AI/ML beam selection algorithm.
- Reduces the need for multiple models by predicting wireless propagation characteristics.
- Utilizes a three-stage autoregressive network for accurate predictions.
- Demonstrates spectral efficiency comparable to traditional methods.
- Addresses practical challenges in deploying AI/ML solutions in diverse antenna environments.
Electrical Engineering and Systems Science > Signal Processing arXiv:2602.18678 (eess) [Submitted on 21 Feb 2026] Title:Heterogeneity-agnostic AI/ML-assisted beam selection for multi-panel arrays Authors:Ibrahim Kilinc, Robert W. Heath Jr View a PDF of the paper titled Heterogeneity-agnostic AI/ML-assisted beam selection for multi-panel arrays, by Ibrahim Kilinc and Robert W. Heath Jr View PDF 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 netwo...