Resource Management for Transmit Power Minimization in UAV-Assisted RIS HetNets Supported by Dual Connectivity

Khalili, Ata; Monfared, Ehsan Mohammadi; Zargari, Shayan; Javan, Mohammad Reza; Mokari, Nader; Jorswieck, Eduard A.

doi:10.1109/twc.2021.3107306

Cited by 89 publications

(40 citation statements)

References 53 publications

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“…The authors of [30] investigated the joint design of resource allocation, beamforming and UAV placement of IoT devices under the constraint of finite block length. By means of jointly designing the speed, trajectory of each UAV and phase shift, the goal of minimizing the total transmit power was studied in [31]. In [32], the authors considered the worst-case secrecy rate minimum through a reliable joint design of beamforming, UAV's transmit power as well as trajectory.…”

Section: A Related Work and Motivationmentioning

confidence: 99%

IRS Empowered UAV Wireless Communication With Resource Allocation, Reflecting Design and Trajectory Optimization

Zhang

et al. 2022

IEEE Trans. Wireless Commun.

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As revolutionary technologies that can actively change the communication link signal, intelligent reflecting surface (IRS) and unmanned aerial vehicle (UAV) have emerged as reliable, economical and convenient wireless communication solutions for a variety of practical scenarios. Therefore, this paper focuses on an IRS empowered UAV downlink communication network, where the dynamic UAV establishes a cascade link via IRS to provide signal enhancement services for multiple users. Considering constraints of transmit power, flight speed and area at the UAV and the reflecting constraints at the IRS, the block coordinate descent (BCD) method based on resource allocation, reflecting design and trajectory optimization is adopted to maximize the sum-rate of all users. The proposed problem is converted by using quadratic transformation and Lagrangian dual transformation. Then applying for the approximate linear method and Iterative Rank Minimization (IRM) to optimize the transmit power of UAV and phase shift of IRS respectively. Since additional reflection propagation paths by IRS, the complexity of the channel model makes the trajectory design difficult. To tackle this problem, this paper proposes a UAV trajectory optimization method based on enhanced reinforcement learning with the fixed initial location and destination. In the end, the convergence of the proposed scheme is effectively verified X. Zhang, H. Zhang, and K. Long are with the

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Section: A Related Work and Motivationmentioning

confidence: 99%

IRS Empowered UAV Wireless Communication With Resource Allocation, Reflecting Design and Trajectory Optimization

Zhang

et al. 2022

IEEE Trans. Wireless Commun.

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“…The work in [77] presented a novel approach of heterogeneous networks (HetNets) supporting dual connectivity, by adopting multiple UAVs as manual relays with IRSs. The authors of [47] investigated how to improve the coverage and reliability of UAV communications systems using IRS-assisted UAVs.…”

Section: Improvement Of Energy Efficiencymentioning

confidence: 99%

“…Because it is difficult to analyze the activation pattern of IoT devices, the authors devised a DRL-based proximal policy optimization (PPO) algorithm to understand the randomness of IoT devices' behavior. In [77], the authors considered multiple UAV-mounted IRSs for a HetNet supported by dual connectivity. The authors developed a DQN-based distributed algorithm to optimize the trajectory and speed of UAVs, phase shift of IRS, subcarrier allocation, and active beamforming at BSs.…”

Section: Algorithmsmentioning

confidence: 99%

A Survey on Intelligent-Reflecting-Surface-Assisted UAV Communications

2022

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Both the unmanned aerial vehicle (UAV) and intelligent reflecting surface (IRS) are attracting growing attention as enabling technologies for future wireless networks. In particular, IRS-assisted UAV communication, which incorporates IRSs into UAV communications, is emerging to overcome the limitations and problems of UAV communications and improve the system performance. This article aims to provide a comprehensive survey on IRS-assisted UAV communications. We first present six representative scenarios that integrate IRSs and UAVs according to the installation point of IRSs and the role of UAVs. Then, we introduce and discuss the technical features of the state-of-the-art relevant works on IRS-assisted UAV communications systems from the perspective of the main performance criteria, i.e., spectral efficiency, energy efficiency, security, etc. We also introduce machine learning algorithms adopted in the previous works. Finally, we highlight technical issues and research challenges that need to be addressed to realize IRS-assisted UAV communications systems.

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“…2) Computational Complexity: The computational complexity of RL-based methods depends on the number of neu- rons, the DNN layers, the state size, and the action space which is described in [14]. It should be noted that the soft actor-critic method has two DNN layers and the computational complexity of each agent as O agent is a function of the complexity of both DNNs, the number of episodes, and the minibatch size.…”

Section: A Decision-making Optimization Problemmentioning

confidence: 99%

Toward a Smart Resource Allocation Policy via Artificial Intelligence in 6G Networks: Centralized or Decentralized?

Nouruzi¹,

Rezaei²,

Khalili³

et al. 2022

Preprint

Self Cite

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In this paper, we design a new smart softwaredefined radio access network (RAN) architecture with important properties like flexibility and traffic awareness for sixth generation (6G) wireless networks. In particular, we consider a hierarchical resource allocation framework for the proposed smart soft-RAN model, where the software-defined network (SDN) controller is the first and foremost layer of the framework. This unit dynamically monitors the network to select a network operation type on the basis of distributed or centralized resource allocation architectures to perform decision-making intelligently. In this paper, our aim is to make the network more scalable and more flexible in terms of achievable data rate, overhead, and complexity indicators. To this end, we introduce a new metric, throughput overhead complexity (TOC), for the proposed machine learning-based algorithm, which makes a trade-off between these performance indicators. In particular, the decision making based on TOC is solved via deep reinforcement learning (DRL), which determines an appropriate resource allocation policy. Furthermore, for the selected algorithm, we employ the soft actor-critic method, which is more accurate, scalable, and robust than other learning methods. Simulation results demonstrate that the proposed smart network achieves better performance in terms of TOC compared to fixed centralized or distributed resource management schemes that lack dynamism. Moreover, our proposed algorithm outperforms conventional learning methods employed in other state-of-the-art network designs.

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Resource Management for Transmit Power Minimization in UAV-Assisted RIS HetNets Supported by Dual Connectivity

Cited by 89 publications

References 53 publications

IRS Empowered UAV Wireless Communication With Resource Allocation, Reflecting Design and Trajectory Optimization

IRS Empowered UAV Wireless Communication With Resource Allocation, Reflecting Design and Trajectory Optimization

A Survey on Intelligent-Reflecting-Surface-Assisted UAV Communications

Toward a Smart Resource Allocation Policy via Artificial Intelligence in 6G Networks: Centralized or Decentralized?

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