A Hardware-Aware Performance Analysis of Machine Learning Models for Sensor-Aided Millimeter-Wave Beam Prediction

International Conference on Communications, pp. 1–7

Abstract

Millimeter-wave (mm-wave) and sub-terahertz communication are cornerstones for 6G networks, but are hindered by their reliance on highly directional beams and the high latency of conventional beam alignment and beam management procedures. Sensor- and machine learning (ML)-based beam prediction offers a low-latency alternative, but remains constrained by hardware limitations at the wireless edge. This paper presents a systematic, hardware-aware analysis of ML models for sensor-aided beam prediction, specifically focusing on LiDAR, spanning deep learning, classical ML, and a novel class of proposed hybrid models that combine a CNN-based feature extractor with lightweight classical classifiers. Using the public DeepSense 6G dataset, models are benchmarked on accuracy, inference latency, memory footprint, and computational complexity. Our results show that classical ML models, particularly linear and tree-based methods, deliver strong accuracy-efficiency trade-offs, achieving up to 95× lower inference latency and 67× smaller memory footprint than CNNs with minimal accuracy loss. The proposed hybrid models further enhance this trade-off, maintaining CNN-level top-5 accuracy while drastically reducing re-training time by up to 20× and enabling fast, environment-specific adaptation. These findings establish classical and hybrid models, supported by efficient preprocessing, as promising candidates for real-time, resource-efficient beam prediction in future 6G edge systems.

Authors 4

1. Barış Sayitoğlu Aachen Chair of Mobile Communications and Computing

   RWTH Aachen University

   Affiliation as printed

   RWTH Aachen University,Chair of Mobile Communications and Computing,Germany

2. Arish Sateesan Aachen Chair of Mobile Communications and Computing

   RWTH Aachen University

   Affiliation as printed

   RWTH Aachen University,Chair of Mobile Communications and Computing,Germany

3. Marina Petrova Aachen Chair of Mobile Communications and Computing

   RWTH Aachen University

   Affiliation as printed

   RWTH Aachen University,Chair of Mobile Communications and Computing,Germany

4. Ljiljana Simić Aachen Chair of Mobile Communications and Computing

   RWTH Aachen University

   Affiliation as printed

   RWTH Aachen University,Chair of Mobile Communications and Computing,Germany