Radar Aided Proactive Blockage Prediction in Real-World Millimeter Wave Systems

Demirhan, Umut; Alkhateeb, Ahmed

doi:10.1109/icc45855.2022.9838438

Cited by 19 publications

(15 citation statements)

References 8 publications

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“…Radar-based blockage prediction is a nascent research direction inspired by the advantageous features of radar technology. To date, only a limited number of studies have explored the effectiveness of this approach [8], [9]. For instance, the work in [8] is utilising radar sensors to enhance the reliability of mmWave systems in indoor environments.…”

Section: Related Workmentioning

confidence: 99%

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Intelligent Beam Blockage Prediction for Seamless Connectivity in Vision-Aided Next-Generation Wireless Networks

Al-Quraan

Khan

Mohjazi

et al. 2023

IEEE Trans. Netw. Serv. Manage.

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The upsurge in wireless devices and real-time service demands force the move to a higher frequency spectrum. Millimetre-wave (mmWave) and terahertz (THz) bands combined with the beamforming technology offer significant performance enhancements for future wireless networks. Unfortunately, shrinking cell coverage and severe penetration loss experienced at higher spectrum render mobility management a critical issue in high-frequency wireless networks, especially optimizing beam blockages and frequent handover (HO). Mobility management challenges have become prevalent in city centres and urban areas. To address this, we propose a novel mechanism driven by exploiting wireless signals and on-road surveillance systems to intelligently predict possible blockages in advance and perform timely HO. This paper employs computer vision (CV) to determine obstacles and users' location and speed. In addition, this study introduces a new HO event, called block event (BLK), defined by the presence of a blocking object and a user moving towards the blocked area. Moreover, the multivariate regression technique predicts the remaining time until the user reaches the blocked area, hence determining best HO decision. Compared to conventional wireless networks without blockage prediction, simulation results show that our BLK detection and proactive HO algorithm achieves 40% improvement in maintaining user connectivity and the required quality of experience (QoE).

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Section: Related Workmentioning

confidence: 99%

“…In addition, radar technology offers lower privacy risk and, most importantly, enables low-latency transmissions as it operates at high-frequency bands. To date, few studies have considered the use of radars to address beam blockages problem in high-frequency networks [8], [9]. However, these studies are preliminary and restricted to specific scenarios.…”

mentioning

confidence: 99%

Intelligent Beam Blockage Prediction for Seamless Connectivity in Vision-Aided Next-Generation Wireless Networks

Al-Quraan

Khan

Mohjazi

et al. 2023

IEEE Trans. Netw. Serv. Manage.

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“…Integrating radar sensing and machine learning could enable the proactive prediction of the link blockages [14]. In particular, radar sensing provides valuable awareness about the communication environment, including the position, shape, and mobility features of the mobile user and the other static/dynamic objects in the environment.…”

Section: Sensing Aided Communicationmentioning

confidence: 99%

“…Real-World Evaluation: To show the potential of the sensing aided blockage prediction with machine learning in real-world application, we collected the scenario 30 of the DeepSense 6G dataset [13] in [14]. The same setup in the previously described beam prediction is used with a static transmitter.…”

Section: Sensing Aided Communicationmentioning

confidence: 99%

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Integrated Sensing and Communication for 6G: Ten Key Machine Learning Roles

Demirhan¹,

Alkhateeb²

2022

Preprint

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Integrating sensing and communication is a defining theme for future wireless systems. This is motivated by the promising performance gains, especially as they assist each other, and by the better utilization of the wireless and hardware resources. Realizing these gains in practice, however, is subject to several challenges where leveraging machine learning can provide a potential solution. This article focuses on ten key machine learning roles for joint sensing and communication, sensingaided communication, and communication-aided sensing systems, explains why and how machine learning can be utilized, and highlights important directions for future research. The article also presents real-world results for some of these machine learning roles based on the large-scale real-world dataset DeepSense 6G, which could be adopted in investigating a wide range of integrated sensing and communication problems.

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