Multi-Task Learning for mmWave Transceiver Beam Prediction
Year: 2025
Type: article
Abstract: Rigorous and reliable alignment of narrow transceiver beams is a requisite for ensuring the highly directional transmission in millimeter-wave (mmWave) communications. Exhaustively testing these narrow beam pairs results in increased reference signal (RS) overhead, latency, and power consumption. In this paper, we propose a centralized multi-task learning (MTL) based beam prediction strategy that ensures a high success rate using measurements from a few site-specific probing beams identified via the proposed uniformly distributed beam relevance and beam significance (UDBRBS) criterion, thereby obviating the need for an exhaustive scan. Performance evaluation over 3rd Generation Partnership Project (3GPP) defined performance indicators demonstrates that the proposed method outperforms existing independent task learning (ITL) and single task learning (STL) beam prediction designs. We further argue that the proposed strategy is highly practical for implementation in fifth generation (5G)-Advanced and sixth generation (6G) communication systems
Institution: Technische Universität Dresden
Language: English
Subfield: Electrical and Electronic Engineering
Field: Engineering
Domain: Physical Sciences
Open Access status: gold
Estimated APC paid: $2,075
Awards: G1099969985, Verbundprojekt: Plattform für zukünftige Kommunikationstechnologien und 6G - 6G-Plattform -, Teilvorhabentitel: Analyse von Konzepten neuer Ecosysteme und zur Migration von Campusnetzen für die Visionsbildung von 6G Netzen, Verbundprojekt: 6G-Access, Network of Networks, Automation & Simplification - 6G-ANNA -
OpenAlex ID: W4411639985