MIMO beam selection in 5G using neural networks

Julius Ruseckas, Gediminas Molis, Hanna Bogucka


In this paper, we consider the cell-discovery problem in 5G millimeter-wave (mmWave) communication systems using multiple-input-multiple-output (MIMO) beam-forming technique. Specifically, we aim at the proper beam selection method using context-awareness of the user equipment to reduce latency in beam/cell identification.
Due to high path-loss in mmWave systems, the beam-forming technique is extensively used to increase Signal-to-Noise Ratio (SNR). When seeking to increase user discovery distance, a narrow beam must be formed. Thus, the number of possible beam orientations and consequently time needed for the discovery increases significantly when a random scanning approach is used. The idea presented here is to reduce latency by employing artificial intelligence (AI) or machine learning (ML) algorithms to guess the best beam orientation using context information from the Global Navigation Satellite System (GNSS), lidars, and cameras, and use the knowledge to swiftly initiate communication with the base station. To this end, here, we propose a simple neural network to predict beam orientation from GNSS and lidar data. Results show that using only GNSS data one can get acceptable
performance for practical applications. This finding can be useful for user devices with limited processing power.

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