Toward Energy-Efficient Multiple IRSs: Federated Learning-Based Configuration Optimization

Li, Lixin; Ma, Donghui; Ren, Huan; Wang, Peijue; Lin, Wensheng; Han, Zhu

doi:10.1109/tgcn.2021.3136306

Cited by 18 publications

(9 citation statements)

References 33 publications

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“…Hence, optimizing the energy efficiency in FL over wireless networks becomes an urgent and critical concern for practical FL implementation on IoT user devices. This issue has attracted significant attention in recent studies [28][29][30][31][32].…”

Section: Related Work and Motivationmentioning

confidence: 99%

Optimization of Energy Efficiency for Federated Learning over Unmanned Aerial Vehicle Communication Networks

Dang,

Shin

2024

Electronics

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Federated learning (FL) is considered a promising machine learning technique that has attracted increasing attention in recent years. Instead of centralizing data in one location for training a global model, FL allows the model training to occur on user devices, such as smartphones, IoT devices, or local servers, thereby respecting data privacy and security. However, implementing FL in wireless communication faces a significant challenge due to the inherent unpredictability and constant fluctuations in channel characteristics. A key challenge in implementing FL over wireless communication lies in optimizing energy efficiency. This holds significant importance, especially considering user devices with restricted power resources. On the other hand, unmanned aerial vehicle (UAV) technologies present a cost-effective solution owing to flexibility and mobility compared to terrestrial base stations. Consequently, the deployment of UAV communication in FL is viewed as a potential approach to deal with the energy efficiency challenge. In this paper, we address the problem of minimizing the total energy consumption of all user equipment (UE) during the training phase of FL over a UAV communication network. Our proposed system facilitates UE to operate concurrently at the same time and frequency, thereby improving bandwidth utilization efficiently. In this paper, we address the problem of minimizing the total energy consumption during the training phase of FL over a UAV communication network. To deal with the proposed nonconvex problem, we propose a novel alternating optimization approach by dividing the problem into two suboptimal problems. We then develop iterative algorithms based on the inner approximation method, yielding at least one locally optimal solution. The numerical results demonstrate the superiority of the proposed algorithm in solving the proposed problem compared to other benchmark algorithms, particularly in determining the optimal trajectory of the UAVs. In addition, we conduct extensive experiments to evaluate how different parameter settings affect performance after implementing the proposed optimization approaches for deploying FL within the UAV communication system. These analyses yield valuable insights into the comparative effectiveness of the proposed optimization algorithms concerning overall energy consumption reduction.

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Section: Related Work and Motivationmentioning

confidence: 99%

Optimization of Energy Efficiency for Federated Learning over Unmanned Aerial Vehicle Communication Networks

Dang,

Shin

2024

Electronics

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“…In this section, we consider the deployment issue of the aerial platform as the outer subproblem of the original optimization in (9). The deployment affects the wireless channels related to the aerial platform, and further impacts the aerial reflection and cooperative jamming.…”

Section: Learning For Deploymentmentioning

confidence: 99%

“…For example, although it is more likely to establish line-of-sight links via RISs deployed on highrise buildings, the overall transmission distance over RISs is usually rather longer as compared with the direct links, and thus may not be able to provide desired performance enhancement. Towards this issue, a direct complement is to increase the number of reflecting elements or deploy more RISs, yet this can be severely restricted by the physical conditions [8], [9]. Also, one may resort to new-type RISs with more desired properties, such as active RISs with amplified reflection, whereas it essentially relies on the advance of material or circuit design that largely goes beyond the scope of conventional communications [10].…”

Section: Introductionmentioning

confidence: 99%

Robust Secrecy via Aerial Reflection and Jamming: Joint Optimization of Deployment and Transmission

Tang¹,

He²,

Dong³

et al. 2023

Preprint

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Reconfigurable intelligent surfaces (RISs) are recognized with great potential to strengthen wireless security, yet the performance gain largely depends on the deployment location of RISs in the network topology. In this paper, we consider the anti-eavesdropping communication established through a RIS at a fixed location, as well as an aerial platform mounting another RIS and a friendly jammer to further improve the secrecy. The aerial RIS helps enhance the legitimate signal and the aerial cooperative jamming is strengthened through the fixed RIS. The security gain with aerial reflection and jamming is further improved with the optimized deployment of the aerial platform. We particularly consider the imperfect channel state information issue and address the worst-case secrecy for robust performance. The formulated robust secrecy rate maximization problem is decomposed into two layers, where the inner layer solves for reflection and jamming with robust optimization, and the outer layer tackles the aerial deployment through deep reinforcement learning. Simulation results show the deployment under different network topologies and demonstrate the performance superiority of our proposal in terms of the worst-case security provisioning as compared with the baselines.

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“…[361] Unsupervised Learning: Unsupervised learning is not datahungry as it does not depend on prior knowledge, unlike supervised learning. Therefore, unsupervised learning FNN PHY key generation [349] Reinforcement learning DDPG Sum-rate maximization [331] PDS-PER Secrecy rate enhancement [337] DDPG Coverage rate maximization [342] DDPG Efficient resource allocation [350] MDP Sum-rate maximization [351] Supervised learning ODE-based CNN Performance maximization [352] CNN Sum-rate maximization [353] CV-DnCNN Performance maximization [354] CNN Achievable rate maximization [355] Unsupervised learning RISBFNN Gain enhancement [344] CNN, FNN Sum-rate maximization [356] DNN Spectral efficiency [160] NN Throughput maximization [357] Federated learning CNN Channel estimation [358] CNN Propagation error reduction [359] DNN Energy-efficient [360] algorithms [362] can be applied in RIS-aided wireless systems to overcome challenges such as channel state detection, [363] beamforming, [364] and transmission power control in device-todevice communications. [365] An unsupervised learning algorithm has been proposed for passive beamforming in RIS-aided wireless communication networks.…”

Section: Other ML Techniquesmentioning

confidence: 99%

“…[367] Therefore, FL algorithms can be applied in RIS-assisted communication systems to overcome challenges such as privacy protection, [368] channel estimation, [358] and energy efficiency. [360] Table 3 summerizes the related works aimed to realize the ML-based RIS-assisted communication. a-c) reproduced with permission.…”

Section: Other ML Techniquesmentioning

confidence: 99%

Recent Progress in Reconfigurable and Intelligent Metasurfaces: A Comprehensive Review of Tuning Mechanisms, Hardware Designs, and Applications

Saifullah

Boag

et al. 2022

Advanced Science

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Intelligent metasurfaces have gained significant importance in recent years due to their ability to dynamically manipulate electromagnetic (EM) waves. Their multifunctional characteristics, realized by incorporating active elements into the metasurface designs, have huge potential in numerous novel devices and exciting applications. In this article, recent progress in the field of intelligent metasurfaces are reviewed, focusing particularly on tuning mechanisms, hardware designs, and applications. Reconfigurable and programmable metasurfaces, classified as space gradient, time modulated, and space-time modulated metasurfaces, are discussed. Then, reconfigurable intelligent surfaces (RISs) that can alter their wireless environments, and are considered as a promising technology for sixth-generation communication networks, are explored. Next, the recent progress made in simultaneously transmitting and reflecting reconfigurable intelligent surfaces (STAR-RISs) that can achieve full-space EM wave control are summarized. Finally, the perspective on the challenges and future directions of intelligent metasurfaces are presented.

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Toward Energy-Efficient Multiple IRSs: Federated Learning-Based Configuration Optimization

Cited by 18 publications

References 33 publications

Optimization of Energy Efficiency for Federated Learning over Unmanned Aerial Vehicle Communication Networks

Optimization of Energy Efficiency for Federated Learning over Unmanned Aerial Vehicle Communication Networks

Robust Secrecy via Aerial Reflection and Jamming: Joint Optimization of Deployment and Transmission

Recent Progress in Reconfigurable and Intelligent Metasurfaces: A Comprehensive Review of Tuning Mechanisms, Hardware Designs, and Applications

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