Transferable Deep Reinforcement Learning Framework for Autonomous Vehicles With Joint Radar-Data Communications

Hieu, Nguyen Quang; Hoang, Dinh Thai; Niyato, Dusit; Wang, Ping; Kim, Dong In; Yuen, Chau

doi:10.1109/tcomm.2022.3182034

Cited by 27 publications

(10 citation statements)

References 32 publications

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“…Therefore, one of the major issues of a JRC node is how to schedule the radar and communications mode adaptively and optimize the resource sharing between them. For example, consider an autonomous vehicle as a JRC node which requires both radar and communications functionalities to navigate efficiently and safely in a complex environment [78]. The radar function in the vehicle detects the presence of pedestrians or other incoming vehicles under bad weather conditions.…”

Section: ) Medium Access Mechanismmentioning

confidence: 99%

Joint Radar and Communications: Architectures, Use Cases, Aspects of Radio Access, Signal Processing, and Hardware

Shatov,

Nuss,

Schieler

et al. 2024

IEEE Access

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Integrated Radar and Communications (JRC) can satisfy the apparent demand for applications based on object detection, tracking, ranging, and positioning. JRC is, therefore, often seen as candidate technology for 6G mobile systems. Implementing JRC will require novel approaches in many research and engineering fields, including protocol design, digital and analog signal processing, and hardware development. The ongoing debates on JRC already include many white papers and research articles ranging in content from very specific technical problems to comprehensive bird's eye-level reviews. This paper represents the work within the Open6GHub research project in Germany, which aims to investigate and implement potential end-to-end solutions for 6G. In this framework, we propose a consolidated vision for potential JRC architectural approaches. The subsequent discussion on integrating radar sensing with communications highlights this technology's state-of-the-art and presents relevant opportunities and challenges.INDEX TERMS 6G, joint communications and sensing (JCAS), joint radar and communications (JRC), integrated sensing and communications (ISAC), radar-communications (RadCom), radio access network (RAN) architecture.

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Section: ) Medium Access Mechanismmentioning

confidence: 99%

Joint Radar and Communications: Architectures, Use Cases, Aspects of Radio Access, Signal Processing, and Hardware

Shatov,

Nuss,

Schieler

et al. 2024

IEEE Access

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show abstract

“…For instance, in [2], Mu et al designed and trained an FCNN to predict the relative angles of the k-th vehicle concerning a Road Side Unit (RSU) at a given time n. The input to the DL model consisted of received echoes from the target vehicles, and the estimated angular parameters was utilized to design an appropriate beamformer. Then, the authors in [37] focused on ISAC-assisted AVs and proposed a function selection process to determine whether communicate or sense should take precedence at a specific time slot. Markov Decision Process (MDP) was employed for this purpose with the aid of two optimization methods: Q-learning and Double Deep Q-Network (DDQN).…”

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

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%

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

Salem,

Quamar,,

Magad

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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“…A DL model is designed in ref. [132] for autonomous vehicles working on a JRC architecture to improve safety and efficiency by utilising both radar detection and data communication functions. A Double Deep Q‐Network with experience replay memory and DNN are used to avoid the slow convergence of the conventional Q ‐learning algorithm.…”

Section: Machine Learning For Non‐rrm Tasks—selected Literature Surveymentioning

confidence: 99%

Artificial intelligence meets radar resource management: A comprehensive background and literature review

Hashmi

Akbar

Adve

et al. 2022

IET Radar Sonar & Navi

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A multi‐function radar is designed to perform disparate functions, such as surveillance, tracking, fire control, amongst others, within a limited resource (time, frequency, and energy) budget. A radar resource management (RRM) module within a radar system makes decisions on prioritisation, parameter selection, and scheduling of associated tasks. However, optimal RRM algorithms are generally computationally complex and operational radars resort to heuristics. On the other hand, algorithms based on artificial intelligence (AI) have been shown to yield near‐optimal radar resource allocation results at manageable computational complexity. This survey study aims at enabling researchers and practitioners better understand the application of AI in RRM‐related problems by providing a thorough literature review of AI‐based RRM techniques. We first provide background concepts in RRM followed by a brief review of Symbolic‐AI techniques for RRM. We mainly focus on the applications of state‐of‐the‐art machine learning techniques to RRM. We emphasise on the recent findings and their potential within practical RRM scenarios for real‐time resource allocation optimisation. The study concludes with a discussion of open research problems and future research directions in the light of the presented survey.

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Transferable Deep Reinforcement Learning Framework for Autonomous Vehicles With Joint Radar-Data Communications

Cited by 27 publications

References 32 publications

Joint Radar and Communications: Architectures, Use Cases, Aspects of Radio Access, Signal Processing, and Hardware

Joint Radar and Communications: Architectures, Use Cases, Aspects of Radio Access, Signal Processing, and Hardware

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

Artificial intelligence meets radar resource management: A comprehensive background and literature review

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