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The 3rd Generation Partnership Project (3GPP) is currently studying machinelearning (ML) for the fifth generation (5G)-Advanced New Radio (NR) airinterface, where spatial and temporal-domain beam prediction are important usecases. With this background, this letter presents a low-complexity ML designthat expedites the spatial-domain beam prediction to reduce the powerconsumption and the reference signaling overhead, which are currentlyimperative for frequent beam measurements. Complexity analysis and evaluationresults showcase that the proposed model achieves state-of-the-art accuracywith lower computational complexity, resulting in reduced power consumption andfaster beam prediction. Furthermore, important observations on thegeneralization of the proposed model are presented in this letter.

A Low-Complexity Machine Learning Design for mmWave Beam Prediction