mmAlert: mmWave Link Blockage Prediction via Passive Sensing
In this letter, the mmAlert system, predicting millimeter wave (mmWave) link blockage during data communication, is elaborated and demonstrated. The passive sensing method is adopted for mobile blocker detection, where two receive beams with separated radio frequency (RF) chains are equipped at the data communication receiver. One receive beam is aligned to the direction of line-of-sight (LoS) path, and the other one periodically sweeps the region close to the LoS path. By comparing the signals received by the above two beams, the Doppler frequencies of the signal scattered from the mobile blocker can be detected. Furthermore, by tracking the Doppler frequencies and the angle-of-arrivals (AoAs) of the scattered signals, the trajectory of the mobile blocker can be estimated, such that the potential link blockage can be predicted by assuming consistent mobile velocity. It is demonstrated via experiments that the mmAlert system can always detect the motions of the walking person close to the LoS path, and predict 90\% of the LoS blockage with sensing time of 1.4 seconds.
Code (0)
등록된 구현이 없습니다.
Tasks
PredictionSimilar Papers 제목 키워드 기반
LiDAR-Aided Mobile Blockage Prediction in Real-World Millimeter Wave Systems
Line-of-sight link blockages represent a key challenge for the reliability and latency of millimeter wave (mmWave) and terahertz (THz) communication networks. This paper proposes to leverage LiDAR sensory data to provide…
DenoisingChannel Prediction for mmWave Ground-to-Air Propagation under Blockage
Ground-to-air (GA) communication using unmanned aerial vehicles (UAVs) has gained popularity in recent years and is expected to be part of 5G networks and beyond. However, the GA links are susceptible to frequent blockag…
Proactively Predicting Dynamic 6G Link Blockages Using LiDAR and In-Band Signatures
Line-of-sight link blockages represent a key challenge for the reliability and latency of millimeter wave (mmWave) and terahertz (THz) communication networks. To address this challenge, this paper leverages mmWave and Li…
DenoisingDeep Learning for Moving Blockage Prediction using Real Millimeter Wave Measurements
Millimeter wave (mmWave) communication is a key component of 5G and beyond. Harvesting the gains of the large bandwidth and low latency at mmWave systems, however, is challenged by the sensitivity of mmWave signals to bl…
BIG-bench Machine LearningTrajectory Tracking for MmWave Communication Systems via Cooperative Passive Sensing
In this paper, a cooperative passive sensing framework for millimeter wave (mmWave) communication systems is proposed and demonstrated in a scenario with one mobile signal blocker. Specifically, in the uplink communicati…