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

TL;DR

A unifying AI/ML-based beam selection algorithm supporting antenna heterogeneity by predicting wireless propagation characteristics independent of antenna configuration is proposed, enabling RSRP calculation and beam selection for arbitrary antenna configurations without retraining or having a separate model for each configuration.

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.

Figures (9)

• Figure 1: Global and local coordinate systems, and the communication system with a BS with single panel and a UE with multi-panel arrays. Antenna panels are UPAs with linearly polarized elements. Each panel has a 3D orientation vector based on the user orientation and the antenna placement.
• Figure 2: Location-based three-stage path information predictor model. There are two common building blocks shown on the left. Dense layers with no activation specified have ReLU activation. The first stage predicts the AoA counts per angle cluster given the location information. Stage 2 performs an autoregressive AoD prediction for each AoA cluster index so that the context information avoids predicting the same AoD indices for the AoA index with more than one occurrence. Finally, stage 3 performs path matrix prediction given AoA, AoD one-hot vectors per path and the location.
• Figure 3: Urban canyon environment with dynamic vehicles. The buses are blockers and cars are users equipped with multi-panel antenna arrays. The solid bold red and green arrows show the orientation of the BS panel and a panel of a UE with multi-panels. Channels for each BS panel and UE panel pairs are generated in Sionna hoydis2023sionna. Different types of traced paths is visible in the legend.
• Figure 4: Normalized signal power comparison between perfect CSI and traced paths at elevation angle $90^\circ$. In (a), the BS beam is fixed to a DFT codebook beam; in (b), the UE beam is fixed. Traced paths from noisy RSRP provide angular power profiles matching perfect CSI.
• Figure 5: Mean SE vs beam coherence time $T_{\text{coh}}$ for $7\times7$ UE panels. Shaded regions show Pareto frontiers for Static Pred and Traced Path with $N_\text{b}$ from 1 to 10 and solid lines use $N_\text{b}=6$. Path tracing achieves within 0.5 b/s/Hz of the genie-aided method under antenna size heterogeneity.