A Low-Complexity Machine Learning Design for mmWave Beam Prediction
Muhammad Qurratulain Khan, Abdo Gaber, Mohammad Parvini, Philipp Schulz, Gerhard Fettweis
Abstract
Machine learning (ML) for fifth generation (5G)-Advanced air interface is currently being studied by the 3rd Generation Partnership Project (3GPP), where millimeter-wave (mmWave) beam prediction is an important use case. Thereby the targets are to reduce reference signal (RS) overhead, latency, and power consumption, which are currently imperative for frequent beam measurements. To this end, a low-complexity ML design is presented, that exploits the spatial correlation between beam qualities to expedite the spatial-domain beam prediction. Evaluation results showcase that the proposal achieves a beam prediction accuracy of 96% with 75% reduction in RS overhead and lower computational complexity as compared to the state of the art. Further, to demonstrate the practicality of the proposed design, we analyze its generalization behavior across different communication scenarios.