6G Wireless Communications and Artificial Intelligence-Controlled Reconfigurable Intelligent Surfaces: From Supervised to Federated Learning
Abstract
:1. Introduction
2. SRE Empowered by RISs
3. Use of AI in Wireless Communications
- (1)
- Network Optimization: AI algorithms are used to analyze reception feedback and predict needs before they arise, optimize the network topology as well as improve efficiency and performance.
- (2)
- Customer Service: AI is used to provide customer support with personalized interactions by employing virtual assistants and chatbots.
- (3)
- Fraud Detection: AI is used to analyze the behavior of users and patterns in data in order to detect fraudulent or exploitative use of the network and protect both the users and the providers.
- (4)
- Targeted Marketing: AI is used to create customized advertising and targeted marketing campaigns to maximize customer engagement and reach.
- (5)
- Security and Surveillance: Like fraud detection, AI is targeting security threats such as cyberattacks against user data and networks.
4. RISs Controlled by AI
4.1. Supervised Learning
4.2. Unsupervised Learning
4.3. Reinforcement Learning
5. The Use of AI for the Creation of SRE
5.1. Federated Learning
5.2. Recent Developments
6. Conclusions and Future Research
Author Contributions
Funding
Data Availability Statement
Conflicts of Interest
References
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Feature | 5G | 6G |
---|---|---|
User experienced data rate | Up to 200 Mbps (20 Gbps peak) | Up to 10 Gbps (1 Tbps + peak) |
Coverage | Primarily terrestrial with limitations | Ubiquitous 3D coverage through space-air-ground integration (99%) |
Connection density | Up to 1 million | 106–108 devices/km2 |
Latency over the air interface | 1–10 ms | 0.1–1 ms |
Frequency bands | Up to 100 GHz | 0.1–10 THz |
Security capacity | Robust (security-as-an-addition) | Enhanced (security-by-design) |
New usage scenarios | Enhanced mobile broadband IoT Mission-critical applications | Integrated AI and Communication Ubiquitous Connectivity Integrated Sensing and Communication |
Ref. | Problem Description | Algorithm Used | Achievement |
---|---|---|---|
Taha et al., 2019 [31] | Reduction in the number of activated elements in a very large RIS | Multi-layer perceptron | Optimization of element activation/deactivation |
Yang et al., 2021 [32] | Reduction in interference | Intelligent Spectrum Learning based on Supervised Deep Learning | Distinguish desired from interfering signals |
Lu et al., 2022 [33] | Maximization of sum-rate for RIS hybrid precoding architecture | Multiple Discrete Classification based on Supervised Deep Learning | Reduction in runtime without any significant performance degradation |
Zhang et al., 2021 [34] | Addition of signal processing units for RIS to overcome the difficulty of acquiring CSI | Backpropagation algorithm based on Supervised Deep Learning | Real-time channel state information acquisition |
Ref. | Problem Description | Algorithm Used | Achievement |
---|---|---|---|
Gao et al., 2020 [35] | Passive beamforming optimization in RIS-assisted systems | Customized deep neural network trained using unsupervised learning | Prediction of phase-shifts in real-time by using unlabeled collected data |
Song et al., 2021 [36] | Beamforming in active and passive MISO systems | Two-stage Unsupervised Deep Learning | Optimization of both transmit beamforming and RIS phase shifts with reduced computational time compared to traditional iterative approaches |
Al-Shaeli et al., 2023 [37] | Passive beamforming for RIS-aided MIMO systems | Neural network—Unsupervised Deep Learning | Low implementation complexity and greater time efficiency than conventional programming strategies |
Ref. | Problem Description | Algorithm Used | Achievement |
---|---|---|---|
Puspitasari and Lee, 2023 [42] | Review paper | Deep RL | Potential of Deep RL for RIS technologies |
Wang and Zhang, 2022 [43] | Real-time phase control of IRS for SRE | Deep RL with DDQN | Model-free control of IRS and improvement of its adaptivity to different channel dynamics |
Saleem et al., 2023 [38] | Network Security for IoT communications under trusted-untrusted device diversity | Deep RL with a DDPG | Maximize security for trusted devices while maintaining the QoS of all devices |
Huang et al., 2020 [39] | Joint design of BS beamforming matrix and IRS phase shift matrix in Massive-MIMO wireless Communications systems | Deep RL with DDPG | Optimal performance in complex communication environments with high efficiency |
Hashemi et al., 2022 [40] | Phase shift design for RIS-aided URLLC systems | Deep RL with TD3 | Reduced overestimation of the action-value function that comes with DDPG |
Huang et al., 2022 [44] | Optimization of multi-hop RIS-aided cooperative networks | Deep RL with PPO | Maximization of data rates in IoT applications |
Nguyen et al., 2022 [41] | Wireless power transfer and RIS-assisted communication with IoT and UAVs | Deep RL with DDPG and PPO | Maintenance of power efficiency while addressing communication quality in dynamic environments |
Ref. | Problem Description | Algorithm Used | Achievement |
---|---|---|---|
Das et al., 2023 [50] | Review paper | FL, FL with Deep Deterministic Policy Gradient (FL-DPPG) | Application of ML techniques in RIS-enhanced systems (focus on IoT) |
Sejan et al., 2022 [51] | Review paper | ML techniques | Application of ML techniques in RIS-enhanced systems (focus on 6G) |
Xiao et al., 2024 [52] | Review paper | OTA-FL | Application of ML techniques in RIS-enhanced systems (focus on OTA-FL performance and data privacy) |
Zhou et al., 2024 [53] | Review paper | FL | Application of ML techniques in RIS-enhanced systems (focus on optimization approaches) |
Ma et al., 2020 [54] | Privacy Preservation | Optimal Beam Reflection (OBR) based on FL | Minimization of centralized data dependencies to ensure robust user privacy |
Li et al., 2020 [55] | Preservation of the privacy of user data in a RIS aided mmWave system | FL | Local models are trained, encrypted, and sent to the central server while ensuring private data remains localized on devices |
Wang et al., 2022 [56] | Reduction in the fading channels effect | FL | Lower training loss and higher test accuracy |
Yang et al., 2022 [57] | Poor spectrum efficiency in RIS-aided networks | FSL | Better spectrum prediction accuracy and enhanced system utility |
Shen et al., 2022 [58] | Great transmission overhead that occurs in RIS-aided communication systems for the collection of Channel Status Information | FDReLNet | Significant reduction in transmission overhead |
Elbir et al., 2022 [59] | Great transmission overhead in MIMO and RIS-aided MIMO systems | FL | Transmission overhead 16 times lower when compared to Centralized Learning |
Zhong et al., 2022 [60] | Users sum rate optimization in a RIS-enhanced NOMA wireless network | FL-DDPG | Improvements in sum rate, training time, and system reliability |
Hu et al., 2021 [61] | Minimization of the energy consumption of devices in a RIS-aided multi-antenna BS system | FL | Energy consumption reduced by 12% compared to FL without the presence of RIS |
Chaaya et al., 2023 [45] | Model training on multi-RIS. Poor performance of heterogeneous communication and OoD environments | FL combined with Nash game theory | Up to 15% better performance in OoD Environments |
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Zaoutis, E.A.; Liodakis, G.S.; Baklezos, A.T.; Nikolopoulos, C.D.; Ioannidou, M.P.; Vardiambasis, I.O. 6G Wireless Communications and Artificial Intelligence-Controlled Reconfigurable Intelligent Surfaces: From Supervised to Federated Learning. Appl. Sci. 2025, 15, 3252. https://doi.org/10.3390/app15063252
Zaoutis EA, Liodakis GS, Baklezos AT, Nikolopoulos CD, Ioannidou MP, Vardiambasis IO. 6G Wireless Communications and Artificial Intelligence-Controlled Reconfigurable Intelligent Surfaces: From Supervised to Federated Learning. Applied Sciences. 2025; 15(6):3252. https://doi.org/10.3390/app15063252
Chicago/Turabian StyleZaoutis, Evangelos A., George S. Liodakis, Anargyros T. Baklezos, Christos D. Nikolopoulos, Melina P. Ioannidou, and Ioannis O. Vardiambasis. 2025. "6G Wireless Communications and Artificial Intelligence-Controlled Reconfigurable Intelligent Surfaces: From Supervised to Federated Learning" Applied Sciences 15, no. 6: 3252. https://doi.org/10.3390/app15063252
APA StyleZaoutis, E. A., Liodakis, G. S., Baklezos, A. T., Nikolopoulos, C. D., Ioannidou, M. P., & Vardiambasis, I. O. (2025). 6G Wireless Communications and Artificial Intelligence-Controlled Reconfigurable Intelligent Surfaces: From Supervised to Federated Learning. Applied Sciences, 15(6), 3252. https://doi.org/10.3390/app15063252