A Deep Reinforcement Learning Approach for Integrated Automotive Radar Sensing and Communication

Xu, Lifan; Zheng, Ruxin; Sun, Shunqiao

doi:10.1109/sam53842.2022.9827815

Cited by 7 publications

(3 citation statements)

References 23 publications

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“…Table III presents a summary of the mentioned ML approaches in ISAC for AVs. Other ISAC-ML based works that considered AV systems include [40] for ISAC-assited AVs, [41]- [43] for vehicular networks, [44]- [46] for vehicle to everything (V2X), [47], [48] for vehicle to infrastructure (V2I), and [49], [50] for UAVs.…”

Section: A Data-driven Approached For Isac-assisted Autonomous Vehicu...mentioning

confidence: 99%

“…The authors proposed three DNNs to convert the features from the radar domain to the communication domain. In [40], an RL-based framework was used to learn a quantized transmit beamforming vector for a sparse transmit array in mobile JRC systems. The authors in [57] addressed the problem of beam alignment in radar-aided mmWave systems by a DL approach to reduce the training overhead.…”

Section: Data-driven Isac-assisted Radar Systemsmentioning

confidence: 99%

“…Table VI summarizes the different ML techniques utilized for beamforming design. Several other ML works considered the problem of beamforming in various ISAC settings, such as [2], [37], [38], [66] in AV networks, [40], [57] in radar, and [55] in radar. More details about these works are found in their respective use case subsections.…”

Section: Data-driven Approaches For Beamforming In Isacmentioning

confidence: 99%

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Data-driven Integrated Sensing and Communication: Recent Advances, Challenges, and Future Prospects

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Quamar,,

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et al. 2023

Preprint

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<p>This paper aims to explore integrating various ML techniques into ISAC systems, covering various applications. These applications span intelligent vehicular networks, encompassing unmanned aerial vehicles (UAVs) and autonomous cars, as well as radar applications, localization and tracking, millimeter wave (mmWave) and Terahertz (THz) communication, and beamforming. The contributions of this paper lie in its comprehensive survey of ML-based works in the ISAC domain and its identification of challenges and future research directions. By synthesizing the existing knowledge and proposing new research avenues, this survey serves as a valuable resource for researchers, practitioners, and stakeholders involved in advancing the capabilities of ISAC systems in the context of 6G networks.</p>

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