Abstract
As the majority of data traffic is generated in indoor environments, millimeter-wave (mm-wave) communications are essential. However, owing to their high directivity and high penetration loss, indoor mm-wave communication is vulnerable to blockages caused by users’ bodies and ambient obstacles. In this study, we investigate an online learning-based method that achieves efficient beam and blockage prediction for indoor mm-wave. The proposed method takes advantage of the fact that the optimal beam index and blockage status depend on the user’s position and corresponding data traffic demand. Simulation results based on 3GPP’s new radio channel and blockage models revealed that the proposed scheme could predict mm-wave blockages with an accuracy exceeding 90%. These results confirmed the viability of the proposed deep neural network (DNN) model for predicting optimal mm-wave beam and spectral efficiencies.
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