User-Centric Phaseless Beam and Blockage Prediction Empowering 5G mmWave Systems

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Abstract
Millimeter wave (mmWave) communication stands as a crucial technology for X-reality and cloud gaming due to its extensive bandwidth. Nevertheless, its user experience suffers from degradation caused by obstacles like humans and objects. Unlike base station (BS) solutions, user-side approaches offer greater flexibility in addressing blockage issues. This paper introduces an innovative beam management architecture named reference-signal-received-power (RSRP)-based blockage-prediction-aided compressive sensing (RSRP-BPCS). Unlike existing methods, our proposed algorithm accounts for real communication system constraints and protocol procedures, ensuring practicality. The key advantage lies in deriving channel information based on RSRP without requiring signal phase knowledge. Additionally, we present two novel strategies on the user side: a multiple-beam-sweeping procedure (MBSP) and a proactive blockage prediction approach. These techniques enable the monitoring of different beam pair links and early anticipation of blockage instances. To achieve rapid recovery, we design an explainable deep learning beam tracking scheme which is joint data and model-based to explore the compressive projection of the power spectrum. The best part is that our strategies are standard-compatible, demanding no supplementary side information, which significantly simplifies implementation with minimal complexity. Through extensive simulations, our proposed algorithm demonstrates exceptional performance in mitigating the impact of blockage, proving its efficacy and practicality.

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