Mobile Reconfigurable Intelligent Surfaces for NOMA Networks: Federated Learning Approaches

Zhong, Ruikang; Liu, Xiao; Liu, Yuanwei; Chen, Yue; Han, Zhu

doi:10.1109/twc.2022.3181747

Cited by 21 publications

(9 citation statements)

References 42 publications

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“…2) FL for RIS-aided Wireless Communications: On the other hand, FL can also be used to optimize the performance of RIS-aided wireless communications. For example, FL is used in [157] and [158] for average rate maximization, in which local models are deployed in user devices and the global model is aggregated by edge servers. In [157], federated neural networks consider sampled channel vectors as input to predict achievable rates.…”

Section: Reinforcement Learning-based Optimizationmentioning

confidence: 99%

“…In [157], federated neural networks consider sampled channel vectors as input to predict achievable rates. FL and DDPG are combined in [158], and the local neural networks used in DDPG will be aggregated and updated.…”

Section: Reinforcement Learning-based Optimizationmentioning

confidence: 99%

“…On one hand, RISs can improve the AirFL performance by minimizing global loss and training gap; on the other hand, FL is used to optimize network performance [157], [158].…”

Section: Actor-critic Learningmentioning

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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Section: Reinforcement Learning-based Optimizationmentioning

confidence: 99%

Section: Reinforcement Learning-based Optimizationmentioning

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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“…Moreover, paper [13] proposed a discard policy to exclude the untrustworthy users from the batch of users taking part to the learning process, whenever an incorrect model was injected by users. The FL was also applied in [14] to support the training needed to handle mobile reconfigurable intelligent surfaces (RISs) and the users power allocation, aiming at improving channel quality, spectrum efficiency and users data rate. Non-orthogonal multiple access techniques were exploited, and a deep-reinforcement learning strategy was applied to optimize the performance.…”

Section: Related Workmentioning

confidence: 99%

A D2D-Aided Federated Learning Scheme With Incentive Mechanism in 6G Networks

Fantacci

Picano

2023

IEEE Access

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Pervasive new era applications are expected to involve massive amount of data to implement intelligent distributed frameworks based on machine learning, supported by sixth generation (6G) networks technology to offer fast and reliable communications. Federated Learning (FL) is rapidly emerging as promising privacy-preserving solution to train machine learning models in a distributed fashion. However, users are often not too inclined to take part in the learning process without receiving compensation. Hence, to overcome this drawback, the functional integration of a proper devices incentive mechanism with an efficient approach for the devices selection in a same FL framework becomes essential. In this regard, this paper proposes a FL framework involving a one-side matching theory-based incentive mechanism to select and encourage users to take part of the process with the aim at minimizing the FL process convergence time and maximizing the users profit. Furthermore, this paper faces with the possibility to overcome bad communication link conditions by resorting to device-to-device communications among users in order to lower the energy wasted and improve the convergence time of the FL process. In particular, an echostate-network, running in local at each user site, has been considered to forecast channel conditions in a reliable manner. Performance evaluation has highlighted the improvements in convergence time and energy consumption of the proposed FL framework in comparison with conventional approaches, hence, highlighting its suitability for applications in the upcoming 6G networks.INDEX TERMS Federated learning, terahertz communications, machine learning.

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“…In this context, they proposed a framework in which the IRS is deployed at an AP and NOMA is used at the AP to serve multiple robots. In a mobile IRS scenario, the authors in [16] proposed a model in which IRSs are mounted on intelligent robots for flexible deployment. A deep deterministic policy gradient (DDPG) framework is used to optimize power allocation and the phase shift.…”

Section: A Related Workmentioning

confidence: 99%

Deep Q‑Learning Based Resource Management in IRS-Assisted VLC Systems

Hammadi¹,

Bariah²,

Muhaidat³

et al. 2022

Preprint

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<p>Visible Light Communication (VLC) is a promising enabling technology for the next-generation wireless networks, as it complements radio-frequency (RF)-based communications by providing wider bandwidth, higher data rates, and immunity to interference from electromagnetic sources. However, due to its unique characteristics, VLC is highly sensitive to the line-of-sight (LoS) blockage. Recently, intelligent reflecting surface (IRS) has been proposed as an innovative solution that dynamically reconfigures the wireless environment. The present contribution proposes a two-stage resource management framework in an indoor VLC system: In the first stage, a maximum possible fairness (MPF) algorithm is presented in order to maximize the fairness amongst the users. In the second stage, deep Q-learning is exploited in order to maximize the overall spectral efficiency (SE). The corresponding numerical results have shown that the proposed DQL-MPF framework exhibits superior performance in terms of the overall SE, achieved at fast convergence rate. More specifically, when the noise power is high and the number of users is relatively large, the DQL-MPF algorithm achieves a more than tenfold overall SE compared to the Baseline scheme. Moreover, the synergy between the MPF and the DQL algorithms is investigated. To this end, we demonstrate that the MPF algorithm maximizes the fairness amongst the users while the DQL algorithm maximizes the overall SE and improves the robustness against the noise.</p>

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Mobile Reconfigurable Intelligent Surfaces for NOMA Networks: Federated Learning Approaches

Cited by 21 publications

References 42 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

A D2D-Aided Federated Learning Scheme With Incentive Mechanism in 6G Networks

Deep Q‑Learning Based Resource Management in IRS-Assisted VLC Systems

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