Joint Trajectory and Beamforming Design for UAV-Enabled Integrated Sensing and Communication

Lyu, Zhonghao; Zhu, Guangxu; Xu, Jie

doi:10.1109/icc45855.2022.9839031

Cited by 13 publications

(4 citation statements)

References 7 publications

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“…On the other side, ISAC is an integrated solution that supports easy and low-complexity hardware deployment onboard battery-constraint agents. For these reasons, the ISAC theoretical framework has been recently applied to UAVs [128]- [135]. For example, in [131], a rotatory-wing UAV transmits the ISAC signal during its flying process, to simultaneously provide downlink communication service to a ground user and sense a target.…”

Section: E Isac Uavmentioning

confidence: 99%

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Advancing UAV Communications: A Comprehensive Survey of Cutting-Edge Machine Learning Techniques

Sun,

Fontanesi,

Canberk

et al. 2024

IEEE Open J. Veh. Technol.

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This paper provides a comprehensive overview of the evolution of Machine Learning (ML), from traditional to advanced, in its application and integration into unmanned aerial vehicle (UAV) communication frameworks and practical applications. The manuscript starts with an overview of the existing research on UAV communication and introduces the most traditional ML techniques. It then discusses UAVs as versatile actors in mobile networks, assuming different roles from airborne user equipment (UE) to base stations (BS). UAV have demonstrated considerable potential in addressing the evolving challenges of next-generation mobile networks, such as enhancing coverage and facilitating temporary hotspots but pose new hurdles including optimal positioning, trajectory optimization, and energy efficiency. We therefore conduct a comprehensive review of advanced ML strategies, ranging from federated learning, transfer and meta-learning to explainable AI, to address those challenges. Finally, the use of stateof-the-art ML algorithms in these capabilities is explored and their potential extension to cloud and/or edge computing based network architectures is highlighted.

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Section: E Isac Uavmentioning

confidence: 99%

“…The authors in [135] consider a UAV equipped with a Uniform Linear Array (ULA) vertically deployed w.r.t. the horizontal plane.…”

Section: E Isac Uavmentioning

confidence: 99%

Advancing UAV Communications: A Comprehensive Survey of Cutting-Edge Machine Learning Techniques

Sun,

Fontanesi,

Canberk

et al. 2024

IEEE Open J. Veh. Technol.

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show abstract

“…Similarly, in [35], a novel integrated periodic sensing and communication mechanism was introduced to achieve a critical balance between radar sensing and communication functionalities. In [36], AUV-assisted ISAC was explored, where multiple AUVs are equipped with dual-functional access points transmitting composite signals for both radar sensing and communication. Transmit beamforming and AUV trajectory were jointly optimized to maximize the average weighted sum rate while meeting the beampattern gain requirement.…”

Section: Related Workmentioning

confidence: 99%

ISAC-Enabled Underwater IoT Network Localization: Overcoming Asynchrony, Mobility, and Stratification Issues

Jehangir,

Majid Ashraf,

Amin Khalil

et al. 2024

IEEE Open J. Commun. Soc.

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In oceanographic and environmental monitoring, achieving precise localization and sensing through Integrated Sensing and Communication (ISAC) within the Internet of Underwater Things (IoUT) networks is paramount. However, ISAC-based IoUT systems present distinctive challenges, including depth-dependent propagation speed, asynchronous clock synchronization, and node mobility. This paper introduces an efficient asynchronous localization method explicitly tailored for ISAC-based IoUT networks, which effectively addresses both the stratification effect and node mobility. Our approach centers on an iterative least squares (LS) algorithm designed to localize Autonomous Underwater Vehicles (AUVs) while carefully considering propagation delay and location estimation. Furthermore, we introduce a mobility model grounded in target sensing mechanisms that rely on AUVs' spatial coordinates and propulsion velocities, thereby enhancing the accuracy of target position estimation. We propose a novel precoding design for sensing using random acoustic signals within IoUT networks. To validate the effectiveness of our method, we conduct comprehensive Monte Carlo simulations and benchmark the results against stateof-the-art techniques. The findings demonstrate a significant reduction in estimation errors, confirming the superior efficiency of our approach compared to existing methods.

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“…Authors in [97] also jointly designed the UAV flight trajectory together with the transmit beamforming for optimizing the communication performance while ensuring the sensing requirements. • Joint Mobility Model and Control Design: According to the unified network management framework, the intersection between the control aspect and high-mobility challenge leads to a set of novel optimization problems where the mobility model of aerial nodes need to be jointly optimized along with controlling policies.…”

Section: Unified Framework: Optimization Problem Analysismentioning

confidence: 99%

Self-Evolving Integrated Vertical Heterogeneous Networks

Farajzadeh

Khoshkholgh²,

Yanıkömeroğlu

et al. 2023

IEEE Open J. Commun. Soc.

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Joint Trajectory and Beamforming Design for UAV-Enabled Integrated Sensing and Communication

Cited by 13 publications

References 7 publications

Advancing UAV Communications: A Comprehensive Survey of Cutting-Edge Machine Learning Techniques

Advancing UAV Communications: A Comprehensive Survey of Cutting-Edge Machine Learning Techniques

ISAC-Enabled Underwater IoT Network Localization: Overcoming Asynchrony, Mobility, and Stratification Issues

Self-Evolving Integrated Vertical Heterogeneous Networks

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