Reconfigurable Intelligence Surface Aided UAV-MEC Systems With NOMA

Xu, Yu; Zhang, Tiankui; Zou, Yixuan; Liu, Yuanwei

doi:10.1109/lcomm.2022.3183285

Cited by 35 publications

(11 citation statements)

References 11 publications

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“…Penalty CPP is used for imperfect CSI in [37], and statistical CSI in [27]. Other applications include non-convex RIS control problems in [32], [78], [106].…”

Section: E Analyses and Discussionmentioning

confidence: 99%

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Knowledge Transfer and Reuse: A Case Study of Ai-Enabled Resource Management in RAN Slicing

Zhang¹,

Erol‐Kantarci²,

Poor³

2023

IEEE Wireless Commun.

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Reconfigurable intelligent surfaces (RISs) have received considerable attention as a key enabler for envisioned 6G networks, for the purpose of improving the network capacity, coverage, efficiency, and security with low energy consumption and low hardware cost. However, integrating RISs into the existing infrastructure greatly increases the network management complexity, especially for controlling a significant number of RIS elements. To unleash the full potential of RISs, efficient optimization approaches are of great importance. This work provides a comprehensive survey on optimization techniques for RISaided wireless communications, including model-based, heuristic, and machine learning (ML) algorithms. In particular, we first summarize the problem formulations in the literature with diverse objectives and constraints, e.g., sum-rate maximization, power minimization, and imperfect channel state information constraints. Then, we introduce model-based algorithms that have been used in the literature, such as alternating optimization, the majorization-minimization method, and successive convex approximation. Next, heuristic optimization is discussed, which applies heuristic rules for obtaining low-complexity solutions. Moreover, we present state-of-the-art ML algorithms and applications towards RISs, i.e., supervised and unsupervised learning, reinforcement learning, federated learning, graph learning, transfer learning, and hierarchical learning-based approaches. Modelbased, heuristic, and ML approaches are compared in terms of stability, robustness, optimality and so on, providing a systematic understanding of these techniques. Finally, we highlight RISaided applications towards 6G networks and identify future challenges.

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“…Penalty CPP is used for imperfect CSI in [37], and statistical CSI in [27]. Other applications include non-convex RIS control problems in [32], [78], [106].…”

Section: E Analyses and Discussionmentioning

confidence: 99%

“…CCP has been applied in [27], [32], [37], [78], [106] for controlling RIS phase shifts. In these works, the joint optimization problem is first decoupled into multiple subproblems using BCD or AO, then penalty CCP is used to solve the RIS phase shifts sub-problem.…”

Section: A Convex-concave Proceduresmentioning

confidence: 99%

Knowledge Transfer and Reuse: A Case Study of Ai-Enabled Resource Management in RAN Slicing

Zhang¹,

Erol‐Kantarci²,

Poor³

2023

IEEE Wireless Commun.

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“…IRSs can improve the performance of UAVs deployed as aerial mobile edge computing (MEC) servers [103]- [108]. For instance, the authors in [103] proposed a dual-RIS MECenabled UAV-assisted network architecture to minimize the energy consumption an Internet of Vehicles (IoV) network.…”

Section: Optimization Of Irs-assisted Uav Systems For Mec Applicationsmentioning

confidence: 99%

“…Similarly, the UAV's trajectory and the IRS's phase shift were jointly optimized in a multi-IRS and multi-UAV system to minimize the UAV's energy consumption, completion time, and maintenance cost in [106], [107]. Finally, IRSs is proven to improve the UAV's computation capacity of an IRS-enabled UAV-assisted MEC system as presented in [108].…”

Section: Optimization Of Irs-assisted Uav Systems For Mec Applicationsmentioning

confidence: 99%

IRS-Empowered 6G Networks: Deployment Strategies, Performance Optimization, and Future Research Directions

et al. 2022

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The performance of the envisioned 6G network is fundamentally constrained by the uncontrollable and random wireless communication channel. Intelligent reflecting surfaces (IRSs) have emerged as one of the potential solutions to overcome this challenge by smartly controlling the incident signal to enhance the energy efficiency and spectrum efficiency of the 6G network. In addition, the future 6G network will incorporate several enabling technologies, including artificial intelligence and machine learning (AI/ML), integrated terrestrial and non-terrestrial (TNT) networks, multi-access edge computing (MEC), non-orthogonal multiple access (NOMA), and terahertz/millimeter wave (THz/mmWave) communication techniques. Therefore, this paper provides a contemporary and comprehensive overview of the envisioned IRS-empowered 6G networks from the perspective of its architecture, deployment strategy, integration of IRS technology with other 6G-enabling technologies, and physical layer security (PLS). Finally, we highlight design challenges and future research directions aimed at improving 6G network performance.INDEX TERMS Intelligent reflecting surfaces, Terahertz communication, Terrestrial and non-terrestrial (TNT) networks, Unmanned Aerial Vehicles, Reinforcement learning, Ultra-reliable and Low-latency communications.

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“…In [30], although the UAV-mounted RIS is deployed to assist the communication between the ground users and an MEC server, the computation capability of UAV is not considered, and the users offload task-input data to the MEC server by OMA protocol which cannot fully utilize the time and frequency resources. Then, in [31], the non-orthogonal multiple access (NOMA) is introduced into the RIS-aided UAV-MEC system, where the aim is to maximize UAV's computation capacity and the simulation results show that the NOMA scheme outperforms OMA. Nevertheless, we note that in [31] the energy consumption of UAV is only imposed as a budget constraint and the position of UAV is fixed.…”

Section: Introductionmentioning

confidence: 99%

Joint Optimization of Resource Allocation, Phase Shift, and UAV Trajectory for Energy-Efficient RIS-Assisted UAV-Enabled MEC Systems

Qin

Song

Hou

et al. 2023

IEEE Trans. on Green Commun. Netw.

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The unmanned aerial vehicle (UAV) enabled mobile edge computing (MEC) has been deemed a promising paradigm to provide ubiquitous communication and computing services for the Internet of Things (IoT). Besides, by intelligently reflecting the received signals, the reconfigurable intelligent surface (RIS) can significantly improve the propagation environment and further enhance the service quality of the UAV-enabled MEC. Motivated by this vision, in this paper, we consider both the amount of completed task bits and the energy consumption to maximize the energy efficiency of the RIS-assisted UAV-enabled MEC systems with non-orthogonal multiple access (NOMA) protocol, where the bit allocation, transmit power, phase shift, and UAV trajectory are jointly optimized by an iterative algorithm with a double-loop structure based on the Dinkelbach's method and block coordinate decent (BCD) technique. Simulation results demonstrate that: 1) our proposed algorithm can achieve higher energy efficiency than baseline schemes while satisfying the task tolerance latency; 2) the energy efficiency first increases and then decreases with the increase of the mission period and the total amount of taskinput bits of IoT devices; 3) the energy efficiencies of schemes with imperfect channel state information (CSI) are lower than corresponding schemes with perfect CSI, and the performance gain of NOMA over OMA diminishes under the imperfect CSI.

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Reconfigurable Intelligence Surface Aided UAV-MEC Systems With NOMA

Cited by 35 publications

References 11 publications

Knowledge Transfer and Reuse: A Case Study of Ai-Enabled Resource Management in RAN Slicing

Knowledge Transfer and Reuse: A Case Study of Ai-Enabled Resource Management in RAN Slicing

IRS-Empowered 6G Networks: Deployment Strategies, Performance Optimization, and Future Research Directions

Joint Optimization of Resource Allocation, Phase Shift, and UAV Trajectory for Energy-Efficient RIS-Assisted UAV-Enabled MEC Systems

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