AI-based Radio Resource Management and Trajectory Design for PD-NOMA Communication in IRS-UAV Assisted Networks

Hariz, Hussein M.; Sheikhzadeh, Saeed; Mokari, Nader; Javan, Mohammad Reza; Abbasi-Arand, B.; Jorswieck, Eduard A.

doi:10.48550/arxiv.2111.03869

Cited by 7 publications

(6 citation statements)

References 38 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In this publication, the authors summarized the UAV-ground NOMA communication into a maxmin problem regarding UAV's energy efficiency. The use of artificial intelligence-driven UAV-NOMA to improve the quality of experiences of terrestrial multi-users is studied in the works [26,27]. Moreover, the authors in [28] investigated the UAV in PD-NOMA with concurrent uplink transmission of the aerial user (AU) and terrestrial user (TU).…”

Section: Related Workmentioning

confidence: 99%

UAV-Assisted Cooperative NOMA and OFDM Communication Systems: Analysis and Optimization

Kieu-Xuan,

Le-Thi

2024

JSAN

View full text Add to dashboard Cite

Utilizing unmanned aerial vehicles (UAVs) to facilitate wireless communication has emerged as a viable and promising strategy to enhance current and prospective wireless systems. This approach offers many advantages by establishing line-of-sight connections, optimizing operational efficiency, and enabling flexible deployment capabilities in various terrains. Thus, in this paper, we investigate UAV communication in a relaying network in which a UAV helps communication between a source and two destination users while flying to a location. To have a complete view of our proposed system, we consider both orthogonal multiple access, such as OFDMs and non-orthogonal multiple access (NOMA) scenarios. Moreover, we apply successive convex optimization (SCO) and the block-coordinate gradient descent (BCGD) for the sum-rate optimization problems to improve the system performance under constraints of total bandwidth and total power at the ground base station and UAV. The experimental results validate that the achievable secrecy rates are notably enhanced using our proposed algorithms and show optimal trends for critical parameters, such as transmit powers, the flight trajectory and speed of the UAV, and resource allocation of OFDM and NOMA.

show abstract

Section: Related Workmentioning

confidence: 99%

UAV-Assisted Cooperative NOMA and OFDM Communication Systems: Analysis and Optimization

Kieu-Xuan,

Le-Thi

2024

JSAN

View full text Add to dashboard Cite

show abstract

“…The NOMA approach examined that it did not require a LoS channel between users and BS. Hariz et al, [121] investigated multiple drones' subcarrier distribution and trajectory to increase user coverage. NOMA examined the LoS link between the receiver and users, while NLoS was between the drone and users.…”

Section: Framework For Fl Meets Irs In Drones To Support 6gmentioning

confidence: 99%

Federated Learning Meets Intelligence Reflection Surface in Drones for Enabling 6G Networks: Challenges and Opportunities

Shvetsov,

Alsamhi,

Hawbani

et al. 2023

IEEE Access

View full text Add to dashboard Cite

The combination of drones and Intelligent Reflecting Surfaces (IRS) have emerged as potential technologies for improving the performance of six Generation (6G) communication networks by proactively modifying wireless communication through smart signal reflection and manoeuvre control. By deploying the IRS on drones, it becomes possible to improve the coverage and reliability of the communication network while reducing energy consumption and costs. Furthermore, integrating IRS with Federated Learning (FL) can further boost the performance of the drone network by enabling collaborative learning among multiple drones, leading to better and more efficient decision-making and holding great promise for enabling 6G communication networks. Therefore, we present a novel framework for FL meets IRS in drones for enabling 6G. In this framework, multiple IRS-equipped drone swarm are deployed to form a distributed wireless network, where FL techniques are used to collaborate with the learning process and optimize the reflection coefficients of each drone-IRS. This allows drone swarm to adapt to changing communication environments and improve the coverage and quality of wireless communication services. Integrating FL and IRS into drones offers several advantages over traditional wireless communication networks, including rapid deployment in emergencies or disasters, improved coverage and quality of communication services, and increased accessibility to remote areas. Finally, we highlight the challenges and opportunities of integrating FL and IRS into drones for researchers interested in drone networks. We also help drive innovation in developing 6G communication networks.

show abstract

“…In [44], the authors investigated a single-antenna transmission of IRS-assisted NOMA network and two RL algorithms were proposed to solve the problem caused by the overhead. In [45], two RL-based algorithms were adopted to solve a resource allocation problem in order to minimize the average age-ofinformation of the users in IoT networks. The freshness of collected data of the IoT devices was improved via optimizing power, sub-carrier, trajectory variables, and the phase shift matrix elements.…”

Section: ) Machine Learning In Airs-noma Networkmentioning

confidence: 99%

Aerial Intelligent Reflecting Surfaces in MIMO-NOMA Networks: Fundamentals, Potential Achievements, and Challenges

Lima

Sena

Dinis

et al. 2022

IEEE Open J. Commun. Soc.

View full text Add to dashboard Cite

Non-orthogonal multiple access (NOMA) technique and unmanned aerial vehicles (UAVs) have been recognized as promising technologies for enabling the stringent requirements of the different network infrastructures expected for the next generation of wireless networks. In parallel, intelligent reconfigurable surfaces (IRSs) have been widely pointed out as an auspicious solution to further improve spectral efficiency, coverage range, and connectivity. By integrating IRS with UAV and NOMA schemes with multiple-input multiple-output (MIMO) it is possible to smartly improve the overall network performance. In order to explore some of these potentials, this paper provides a comprehensive discussion about the interplay of aerial IRS in MIMO-NOMA (AIRS-NOMA) networks, as well its architecture, functionality principles, and performance gains. In particular, attractive gains related to the data rate maximization, user fairness, energy efficiency, and coverage range are highlighted. Simulation results are provided to support our insightful discussions, in which it is revealed that the performance gains of AIRS-NOMA networks are superior when compared to terrestrial deployment. In addition, to guide new studies perspectives, it is addressed some issues and research opportunities associated with this potential integration.

show abstract

AI-based Radio Resource Management and Trajectory Design for PD-NOMA Communication in IRS-UAV Assisted Networks

Cited by 7 publications

References 38 publications

UAV-Assisted Cooperative NOMA and OFDM Communication Systems: Analysis and Optimization

UAV-Assisted Cooperative NOMA and OFDM Communication Systems: Analysis and Optimization

Federated Learning Meets Intelligence Reflection Surface in Drones for Enabling 6G Networks: Challenges and Opportunities

Aerial Intelligent Reflecting Surfaces in MIMO-NOMA Networks: Fundamentals, Potential Achievements, and Challenges

Contact Info

Product

Resources

About