Machine Learning Technique for Beam Management in 5G NR RAN at mmWave Frequencies
Abstract: Ericsson has an interest in investigating if the fast-growing concept known as machine learning can be applied to beam management, in a 5G NR environment using mmWave frequencies. Because of the high path-loss at mmWave frequencies and high throughput demands of 5G NR systems it is crucial to the UE to always stay connected to the most suitable beam, to provide highest possible throughput. To obtain the required fine alignment of each single beam, optimization of beam management operations, such as beam tracking is essential. The type of machine learning algorithm used is called reinforcement learning. The algorithm will aim to always connect the UE to the most suitable beam - by comparing RSRP values from a selection of beams, that are picked based on the current serving beam. The machine learning algorithm will initially pick a candidate beam set based on baseline (which is explicitly programmed), however after multiple iterations, when the algorithm is considered experienced, decisions will instead be based on machine learning. The algorithm will be trained from scratch over 50 different seeds i.e. 50 different environments with different properties to increase the reliability of the performance of the machine learning. The performance of the machine learning algorithm will be evaluated by comparing the cell downlink throughput of machine learning and baseline. When reviewing the result, it is clearly illustrated that reinforcement learning can be applied to beam management in mmWave environment to boost the average cell downlink throughput compared to baseline.
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