RIS-Driven Resource Allocation Strategies for Diverse Network Environments: A Comprehensive Review

AI; beyond 5G and 6G; DRL; intelligent reflecting surfaces; p smart radio environment; reconfigurable intelligent surfaces; Beyond 5g and 6g; Intelligent reflecting surface; Network environments; P smart radio environment; Radio environment; Reconfigurable; Reconfigurable intelligent surface; Reflecting surface; Resources allocation; Electrical and Electronic Engineering

Abstract :

[en] This comprehensive survey examines how reconfigurable intelligent surfaces (RIS) revolutionize resource allocation in various network frameworks. It begins by establishing a theoretical foundation with an overview of RIS technologies, including passive RIS, active RIS, and simultaneously transmitting and reflecting RIS (STAR-RIS). The core of the survey focuses on RIS's role in optimizing resource allocation within single-input multiple-output (SIMO), multiple-input single-output (MISO), and multiple-input multiple-output (MIMO) systems. It further explores RIS integration in complex network environments, such as heterogeneous wireless networks (HetNets) and non-orthogonal multiple access (NOMA) frameworks. Additionally, the survey investigates RIS applications in advanced communication domains like terahertz (THz) networks, vehicular communication (VC), and unmanned aerial vehicle (UAV) communications, highlighting the synergy between RIS and artificial intelligence (AI) for enhanced network efficiency. Summary tables provide comparative insights into various schemes. The survey concludes with lessons learned, future research directions, and challenges, emphasizing critical open issues.

Disciplines :

Electrical & electronics engineering

Author, co-author :

Ahmed, Manzoor; The School of Computer and Information Science and Also With the Institute for AI Industrial Technology Research, Hubei Engineering University, Xiaogan, China

Xu, Fang; The School of Computer and Information Science and Also With the Institute for AI Industrial Technology Research, Hubei Engineering University, Xiaogan, China ; The School of Computer Science and Information Engineering, Hubei University, Wuhan, China

Lyu, Yuanlin ; The School of Computer Science and Information Engineering, Hubei University, Wuhan, China ; The College of Technology, Hubei Engineering University, Xiaogan, China

Soofi, Aized Amin; The Department of Computer Science, National University of Modern Languages Faisalabad, Pakistan

Li, Yongxiao; The Electronics Engineering Department, Beijing University of Posts and Telecommunications, Beijing, China

Khan, Feroz; The Electronics Engineering Department, Beijing University of Posts and Telecommunications, Beijing, China

KHAN, Wali Ullah ; University of Luxembourg > Interdisciplinary Centre for Security, Reliability and Trust (SNT) > SigCom

Sheraz, Muhammad; The Centre for Wireless Technology, Faculty of Engineering, Multimedia University, Cyberjaya, Malaysia

Chuah, Teong Chee; The Centre for Wireless Technology, Faculty of Engineering, Multimedia University, Cyberjaya, Malaysia

Deng, Min; The School of Computer and Information Science and Also With the Institute for AI Industrial Technology Research, Hubei Engineering University, Xiaogan, China

External co-authors :

yes

Language :

English

Title :

RIS-Driven Resource Allocation Strategies for Diverse Network Environments: A Comprehensive Review

Publication date :

June 2025

Journal title :

Transactions on Emerging Telecommunications Technologies

eISSN :

2161-3915

Publisher :

John Wiley and Sons Inc

Volume :

Issue :

Peer reviewed :

Peer Reviewed verified by ORBi

Additional URL :

https://onlinelibrary.wiley.com/doi/pdf/10.1002/ett.70160

Funding text :

This research was supported by the MOE (Ministry of Education of China) Project of Humanities and Social Sciences (23YJAZH169), the Hubei Provincial Department of Education Outstanding Youth Scientific Innovation Team Support Foundation (T2020017).This research was supported by the MOE (Ministry of Education of China) Project of Humanities and Social Sciences (23YJAZH169), the Hubei Provincial Department of Education Outstanding Youth Scientific Innovation Team Support Foundation (T2020017). Funding:

Data Set :

https://onlinelibrary.wiley.com/doi/abs/10.1002/ett.70160

Available on ORBilu :

since 07 December 2025

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Bibliography

Z. Ning, T. Li, Y. Wu, et al., “6g Communication New Paradigm: The Integration of Unmanned Aerial Vehicles and Intelligent Reflecting Surfaces,” IEEE Communication Surveys and Tutorials (2025): 1, https://doi.org/10.1109/COMST.2025.3526251.
M. Ahmed, M. Shahwar, F. Khan, et al., “Noma-Based Backscatter Communications: Fundamentals, Applications, and Advancements,” IEEE Internet of Things Journal 11, no. 11 (2024): 19303–19327.
M. Jung, W. Saad, M. Debbah, and C. S. Hong, “On the Optimality of Reconfigurable Intelligent Surfaces (RISs): Passive Beamforming, Modulation, and Resource Allocation,” IEEE Transactions on Wireless Communications 20, no. 7 (2021): 4347–4363.
A. Umer, I. Müürsepp, M. M. Alam, and H. Wymeersch, “Reconfigurable Intelligent Surfaces in 6g Radio Localization: A Survey of Recent Developments, Opportunities, and Challenges,” IEEE Communication Surveys and Tutorials (2025): 1.
M. Jian, G. C. Alexandropoulos, E. Basar, et al., “Reconfigurable Intelligent Surfaces for Wireless Communications: Overview of Hardware Designs, Channel Models, and Estimation Techniques,” Intelligent and Converged Networks 3, no. 1 (2022): 1–32.
L. You, J. Xiong, D. W. K. Ng, C. Yuen, W. Wang, and X. Gao, “Energy Efficiency and Spectral Efficiency Tradeoff in RIS-Aided Multiuser MIMO Uplink Transmission,” IEEE Transactions on Signal Processing 69 (2021): 1407–1421.
W. U. Khan, E. Lagunas, Z. Ali, et al., “Opportunities for Physical Layer Security in Uav Communication Enhanced With Intelligent Reflective Surfaces,” IEEE Wireless Communications 29, no. 6 (2022): 22–28.
W. U. Khan, M. Ahmed, C. K. Sheemar, et al., “Survey on Beyond Diagonal Ris Enabled 6g Wireless Networks: Fundamentals, Recent Advances, and Challenges. arXiv Preprint arXiv:2503.08423,” 2025.
M. Ahmed, S. Raza, A. A. Soofi, et al., “Active Reconfigurable Intelligent Surfaces: Expanding the Frontiers of Wireless Communication-A Survey,” IEEE Communication Surveys and Tutorials 27, no. 2 (2025): 839–869.
W. U. Khan, A. Mahmood, A. Bozorgchenani, et al., “Opportunities for Intelligent Reflecting Surfaces in 6g Empowered v2x Communications,” IEEE Internet of Things Magazine 7, no. 6 (2024): 72–79.
M. Ahmed, F. Xu, A. Wahid, et al., “A Comprehensive Survey of Artificial Intelligence Advances in Ris-Assisted Wireless Networks. Authorea Preprints,” 2024.
M. Ahmed, S. Raza, A. A. Soofi, et al., “A Survey on Reconfigurable Intelligent Surfaces Assisted Multi-Access Edge Computing Networks: State of the Art and Future Challenges,” Computer Science Review 54 (2024): 100668.
A. Almohamad, A. M. Tahir, A. Al-Kababji, et al., “Smart and Secure Wireless Communications via Reflecting Intelligent Surfaces: A Short Survey,” IEEE Open Journal of the Communications Society 1 (2020): 1442–1456.
W. U. Khan, E. Lagunas, A. Mahmood, M. Asif, M. Ahmed, and S. Chatzinotas, “Integration of Beyond Diagonal Ris and Uavs in 6g Ntns: Enhancing Aerial Connectivity. arXiv Preprint arXiv:2409.06073,” 2024.
Z. Ali, W. U. Khan, M. Asif, et al., “Enhanced Learning-Based Hybrid Optimization Framework for Rsma-Aided Underlay Leo Communication With Non-Collaborative Terrestrial Primary Network,” IEEE Transactions on Communications 73, no. 4 (2025): 2176–2190.
M. Deng, M. Ahmed, A. Wahid, et al., “Reconfigurable Intelligent Surfaces Enabled Vehicular Communications: A Comprehensive Survey of Recent Advances and Future Challenges,” IEEE Transactions on Intelligent Vehicles (2024): 1–28, https://doi.org/10.1109/TIV.2024.3476934.
W. U. Khan, A. Mahmood, M. A. Jamshed, E. Lagunas, M. Ahmed, and S. Chatzinotas, “Beyond Diagonal Ris for 6g Non-Terrestrial Networks: Potentials and Challenges,” IEEE Network 39, no. 1 (2025): 80–89.
W. U. Khan, Z. Ali, A. Mahmood, E. Lagunas, S. T. Shah, and S. Chatzinotas, “Transmissive Ris-Assisted Cognitive Radio Enabled Two-Tier Noma Satellite Networks. Authorea Preprints,” 2024.
M. Asif, X. Bao, A. Ranjha, et al., “Leveraging Ris in Consumer-Centric 6g Networks: Efficient Resource Allocation in Rsma-Based Swipt Systems Under Hardware Impairments,” IEEE Transactions on Consumer Electronics (2024): 1, https://doi.org/10.1109/TCE.2024.3512939.
P. Xu, W. Niu, G. Chen, Y. Li, and Y. Li, “Performance Analysis of RIS-Assisted Systems With Statistical Channel State Information,” IEEE Transactions on Vehicular Technology 71, no. 1 (2022): 1089–1094.
J. Chen, Y.-C. Liang, H. V. Cheng, and W. Yu, “Channel Estimation for Reconfigurable Intelligent Surface Aided Multi-User mmWave MIMO Systems,” IEEE Transactions on Wireless Communications 22, no. 10 (2023): 6853–6869.
Y. Liu, X. Mu, X. Liu, M. Di Renzo, Z. Ding, and R. Schober, “Reconfigurable Intelligent Surface-Aided Multi-User Networks: Interplay Between NOMA and RIS,” IEEE Wireless Communications 29, no. 2 (2022): 169–176.
C. Huang, Z. Yang, G. C. Alexandropoulos, et al., “Multi-Hop RIS-Empowered Terahertz Communications: A DRL-Based Hybrid Beamforming Design,” IEEE Journal on Selected Areas in Communications 39, no. 6 (2021): 1663–1677.
W. U. Khan, C. K. Sheemar, Z. Abdullah, E. Lagunas, and S. Chatzinotas, “Beyond Diagonal Irs Assisted Ultra Massive Thz Systems: A Low Resolution Approach,” in 2024 IEEE 35th International Symposium on Personal, Indoor and Mobile Radio Communications (PIMRC) (IEEE, 2024), 1–5.
A. Bansal, N. Agrawal, K. Singh, C.-P. Li, and S. Mumtaz, “RIS Selection Scheme for UAV-Based Multi-RIS-Aided Multiuser Downlink Network With Imperfect and Outdated CSI,” IEEE Transactions on Communications 71, no. 8 (2023): 4650–4664.
Z. Ali, M. Asif, W. U. Khan, et al., “Hybrid Optimization for Noma-Based Transmissive-Ris Mounted Uav Networks,” IEEE Transactions on Consumer Electronics (2025): 1, https://doi.org/10.1109/TCE.2025.3528929.
M. Asif, X. Bao, A. Ihsan, et al., “Noma-Based Ze-Ris Empowered Backscatter Communication With Energy-Efficient Resource Management,” IEEE Transactions on Communications (2025): 1, https://doi.org/10.1109/TCOMM.2025.3545702.
T. Bai, C. Pan, C. Han, and L. Hanzo, “Reconfigurable Intelligent Surface Aided Mobile Edge Computing,” IEEE Wireless Communications 28, no. 6 (2021): 80–86.
R. Zhong, Y. Liu, X. Mu, Y. Chen, and L. Song, “AI Empowered RIS-Assisted NOMA Networks: Deep Learning or Reinforcement Learning?,” IEEE Journal on Selected Areas in Communications 40, no. 1 (2022): 182–196.
Y.-C. Liang, R. Long, Q. Zhang, J. Chen, H. V. Cheng, and H. Guo, “Large Intelligent Surface/Antennas (LISA): Making Reflective Radios Smart,” Journal of Communications and Information Networks 4, no. 2 (2019): 40–50.
M. D. Renzo, M. Debbah, D.-T. Phan-Huy, et al., “Smart Radio Environments Empowered by Reconfigurable AI Meta-Surfaces: An Idea Whose Time has Come,” EURASIP Journal on Wireless Communications and Networking 2019, no. 1 (2019): 1–20, https://doi.org/10.1186/s13638-019-1438-9.
M. di Renzo, A. Zappone, M. Debbah, et al., “Smart Radio Environments Empowered by Reconfigurable Intelligent Surfaces: How It Works, State of Research, and the Road Ahead,” IEEE Journal on Selected Areas in Communications 38, no. 11 (2020): 2450–2525.
S. Gong, X. Lu, D. T. Hoang, et al., “Toward Smart Wireless Communications via Intelligent Reflecting Surfaces: A Contemporary Survey,” IEEE Communication Surveys and Tutorials 22, no. 4 (2020): 2283–2314.
S. Kisseleff, W. A. Martins, H. Al-Hraishawi, S. Chatzinotas, and B. Ottersten, “Reconfigurable Intelligent Surfaces for Smart Cities: Research Challenges and Opportunities,” IEEE Open Journal of the Communications Society 1 (2020): 1781–1797.
M. A. ElMossallamy, H. Zhang, L. Song, K. G. Seddik, Z. Han, and G. Y. Li, “Reconfigurable Intelligent Surfaces for Wireless Communications: Principles, Challenges, and Opportunities,” IEEE Transactions on Cognitive Communications and Networking 6, no. 3 (2020): 990–1002.
Q. Wu, S. Zhang, B. Zheng, C. You, and R. Zhang, “Intelligent Reflecting Surface-Aided Wireless Communications: A Tutorial,” IEEE Transactions on Communications 69, no. 5 (2021): 3313–3351.
W. Long, R. Chen, M. Moretti, W. Zhang, and J. Li, “A Promising Technology for 6G Wireless Networks: Intelligent Reflecting Surface,” Journal of Communications and Information Networks 6, no. 1 (2021): 1–16.
S. Kisseleff, S. Chatzinotas, and B. Ottersten, “Reconfigurable Intelligent Surfaces in Challenging Environments: Underwater, Underground, Industrial and Disaster,” IEEE Access 9 (2021): 150214–150233.
X. Yuan, Y.-J. A. Zhang, Y. Shi, W. Yan, and H. Liu, “Reconfigurable-Intelligent-Surface Empowered Wireless Communications: Challenges and Opportunities,” IEEE Wireless Communications 28, no. 2 (2021): 136–143.
M. E. Arslan, H. Rezaei, G. Yammine, et al., “Design Considerations and Propagation Properties Using an Sdn-Controlled Ris for Wireless Backhauling,” IEEE Transactions on Antennas and Propagation 73, no. 4 (2025): 2451–2461.
H. Zhang, B. Di, K. Bian, Z. Han, H. V. Poor, and L. Song, “Toward Ubiquitous Sensing and Localization With Reconfigurable Intelligent Surfaces,” Proceedings of the IEEE 110, no. 9 (2022): 1401–1422.
E. Björnson, H. Wymeersch, B. Matthiesen, P. Popovski, L. Sanguinetti, and E. de Carvalho, “Reconfigurable Intelligent Surfaces: A Signal Processing Perspective With Wireless Applications,” IEEE Signal Processing Magazine 39, no. 2 (2022): 135–158.
B. Zheng, C. You, W. Mei, and R. Zhang, “A Survey on Channel Estimation and Practical Passive Beamforming Design for Intelligent Reflecting Surface Aided Wireless Communications,” IEEE Communication Surveys and Tutorials 24, no. 2 (2022): 1035–1071.
S. Aboagye, A. R. Ndjiongue, T. M. N. Ngatched, O. A. Dobre, and H. V. Poor, “RIS-Assisted Visible Light Communication Systems: A Tutorial,” IEEE Communication Surveys and Tutorials 25, no. 1 (2023): 251–288.
A. Tishchenko, M. Khalily, A. Shojaeifard, et al., “The Emergence of Multi-Functional and Hybrid Reconfigurable Intelligent Surfaces for Integrated Sensing and Communications -a Survey,” IEEE Communication Surveys and Tutorials (2025): 1, https://doi.org/10.1109/COMST.2024.3519785.
H. Zhang, S. Zeng, B. Di, et al., “Intelligent Omni-Surfaces for Full-Dimensional Wireless Communications: Principles, Technology, and Implementation,” IEEE Communications Magazine 60, no. 2 (2022): 39–45.
H. Zhang and B. Di, “Intelligent Omni-Surfaces: Simultaneous Refraction and Reflection for Full-Dimensional Wireless Communications,” IEEE Communication Surveys and Tutorials 24, no. 4 (2022): 1997–2028.
S. K. Das, F. Benkhelifa, Y. Sun, et al., “Comprehensive Review on ML-Based RIS-Enhanced IoT Systems: Basics, Research Progress and Future Challenges,” Computer Networks 224 (2023): 109581, https://doi.org/10.1016/j.comnet.2023.109581.
S. F. Drampalou, N. I. Miridakis, H. C. Leligou, and P. A. Karkazis, “A Survey on Optimal Channel Estimation Methods for RIS-Aided Communication Systems,” Signals 4, no. 1 (2023): 208–234, https://doi.org/10.3390/signals4010012.
W. Khalid, M. A. U. Rehman, T. Van Chien, Z. Kaleem, H. Lee, and H. Yu, “Reconfigurable Intelligent Surface for Physical Layer Security in 6G-IoT: Designs, Issues, and Advances,” IEEE Internet of Things Journal 11, no. 2 (2024): 3599–3613.
A. A. Puspitasari and B. M. Lee, “A Survey on Reinforcement Learning for Reconfigurable Intelligent Surfaces in Wireless Communications,” Sensors 23, no. 5 (2023): 2554, https://doi.org/10.3390/s23052554.
N. Rahmatov and H. Baek, “RIS-Carried UAV Communication: Current Research, Challenges, and Future Trends,” ICT Express 9, no. 5 (2023): 961–973, https://doi.org/10.1016/j.icte.2023.03.004.
M. Ahmed, A. Wahid, S. S. Laique, et al., “A Survey on STAR-RIS: Use Cases, Recent Advances, and Future Research Challenges,” IEEE Internet of Things Journal 10, no. 16 (2023): 14689–14711.
M. Ahmed, A. A. Soofi, S. Raza, et al., “Advancements in Ris-Assisted Uav for Empowering Multiaccess Edge Computing: A Survey,” IEEE Internet of Things Journal 12, no. 6 (2025): 6325–6346.
N. Baskar, P. Selvaprabhu, V. B. Kumaravelu, et al., “A Survey on Resource Allocation and Energy Efficient Maximization for IRS-Aided MIMO Wireless Communication,” IEEE Access 12 (2024): 85423–85454.
H. Zhou, M. Erol-Kantarci, Y. Liu, and H. V. Poor, “A Survey on Model-Based, Heuristic, and Machine Learning Optimization Approaches in RIS-Aided Wireless Networks,” IEEE Communication Surveys and Tutorials 26, no. 2 (2024): 781–823.
Y. Liu, X. Liu, X. Mu, et al., “Reconfigurable Intelligent Surfaces: Principles and Opportunities,” IEEE Communication Surveys and Tutorials 23, no. 3 (2021): 1546–1577.
S. Hassouna, M. A. Jamshed, J. Rains, et al., “A Survey on Reconfigurable Intelligent Surfaces: Wireless Communication Perspective,” IET Communications 17, no. 5 (2023): 497–537.
S. Basharat, M. Khan, M. Iqbal, U. S. Hashmi, S. A. R. Zaidi, and I. Robertson, “Exploring Reconfigurable Intelligent Surfaces for 6G: State-Of-The-Art and the Road Ahead,” IET Communications 16, no. 13 (2022): 1458–1474.
P. Sun, E. Liu, W. Ni, et al., “Reconfigurable Intelligent Surface-Assisted Wireless Federated Learning With Imperfect Aggregation,” IEEE Transactions on Communications 73, no. 2 (2025): 1058–1071.
Y. Wang, G. He, B. Yang, Z. Hao, Q. Guo, and Z. Ma, “End-To-End Throughput Maximization Oriented Resource Allocation in Ris-Assisted Mmwave Iabn Using Nonorthogonal Multiple Access,” IEEE Internet of Things Journal 11, no. 13 (2024): 23282–23296.
X. Mu, Y. Liu, L. Guo, J. Lin, and R. Schober, “Simultaneously Transmitting and Reflecting (STAR) RIS Aided Wireless Communications,” IEEE Transactions on Wireless Communications 21, no. 5 (2022): 3083–3098.
B. Rana, S.-S. Cho, and I.-P. Hong, “Review Paper on Hardware of Reconfigurable Intelligent Surfaces,” IEEE Access 11 (2023): 29614–29634.
Z. Zhang, L. Dai, X. Chen, et al., “Active RIS vs. Passive RIS: Which Will Prevail in 6G?,” IEEE Transactions on Communications 71, no. 3 (2023): 1707–1725.
K. Zhi, C. Pan, H. Ren, K. K. Chai, and M. Elkashlan, “Active RIS Versus Passive RIS: Which Is Superior With the Same Power Budget?,” IEEE Communications Letters 26, no. 5 (2022): 1150–1154.
H. Ren, Z. Chen, G. Hu, Z. Peng, C. Pan, and J. Wang, “Transmission Design for Active RIS-Aided Simultaneous Wireless Information and Power Transfer,” IEEE Wireless Communications Letters 12, no. 4 (2023): 600–604.
J. Rao, Y. Zhang, S. Tang, Z. Li, C.-Y. Chiu, and R. Murch, “An Active Reconfigurable Intelligent Surface Utilizing Phase-Reconfigurable Reflection Amplifiers,” IEEE Transactions on Microwave Theory and Techniques 71, no. 7 (2023): 3189–3202.
R. Schroeder, J. He, and M. Juntti, “Passive RIS vs. Hybrid RIS: A Comparative Study on Channel Estimation,” in 2021 IEEE 93rd Vehicular Technology Conference (VTC2021-Spring) (IEEE, 2021), 1–7.
S. Zeng, H. Zhang, B. Di, et al., “Reconfigurable Intelligent Surfaces in 6G: Reflective, Transmissive, or Both?,” IEEE Communications Letters 25, no. 6 (2021): 2063–2067.
J. Y. Dai, J. Zhao, Q. Cheng, and T. J. Cui, “Independent Control of Harmonic Amplitudes and Phases via a Time-Domain Digital Coding Metasurface,” Light: Science & Applications 7, no. 1 (2018): 90, https://doi.org/10.1038/s41377-018-0092-z.
J. Tang, M. Cui, S. Xu, L. Dai, F. Yang, and M. Li, “Transmissive RIS for B5G Communications: Design, Prototyping, and Experimental Demonstrations,” IEEE Transactions on Communications 71, no. 11 (2023): 6605–6615.
A. Subhash, A. Kammoun, A. Elzanaty, S. Kalyani, Y. H. Al-Badarneh, and M.-S. Alouini, “Optimal Phase Shift Design for Fair Allocation in RIS-Aided Uplink Network Using Statistical CSI,” IEEE Journal on Selected Areas in Communications 41, no. 8 (2023): 2461–2475.
S. Aghashahi, A. Tadaion, Z. Zeinalpour-Yazdi, M. B. Mashhadi, and A. Elzanaty, “EMF-Aware Energy Efficient MU-SIMO Systems With Multiple RISs,” IEEE Transactions on Vehicular Technology 73, no. 5 (2024): 7339–7344.
Y. Li, Y. Zou, J. Zhu, et al., “Sum Secrecy Rate Maximization for Active RIS-Assisted Uplink SIMO-NOMA Networks,” IEEE Communications Letters 28, no. 3 (2024): 722–726.
Y. Gao, C. Lu, Y. Lian, et al., “QoS-Aware Resource Allocation of RIS-Aided Multi-User MISO Wireless Communications,” IEEE Transactions on Vehicular Technology 73, no. 2 (2024): 2872–2877.
S. Bidabadi, M. Ahmed Ouameur, M. Bagaa, and D. Massicotte, “Energy Efficient Resource Allocation for Re-Configurable Intelligent Surface-Assisted Wireless Networks,” EURASIP Journal on Wireless Communications and Networking 2023, no. 1 (2023): 89, https://doi.org/10.1186/s13638-023-02296-7.
R. K. Fotock, A. Zappone, and M. D. Renzo, “Energy Efficiency Optimization in RIS-Aided Wireless Networks: Active Versus Nearly-Passive RIS With Global Reflection Constraints,” IEEE Transactions on Communications 72, no. 1 (2024): 257–272.
Y. Yang, H. Hu, F. Zhou, L. Yang, and R. Q. Hu, “Robust Beamforming and Phase Shift Optimization for RIS-Assisted UAV Multiuser MISO Communication Networks,” IEEE Transactions on Vehicular Technology 73, no. 10 (2024): 15687–15692.
H. Li, H. Wang, and P. Li, “Weighted Sum-Rate Maximization for Collaborative-RIS-Assisted Downlink MISO System,” IEEE Wireless Communications Letters 13, no. 6 (2024): 1740–1744.
S. Palmucci, A. Abrardo, and M. Moretti, “Optimal RIS Allocation in Multi-User MIMO Communications,” in 2023 21st Mediterranean Communication and Computer Networking Conference (MedComNet) (IEEE, 2023), 188–195.
K. Singh and M. Katwe, “Energy-Efficient Resource Allocation and User Grouping for Multi-IRS Aided MU-MIMO System,” Physical Communication 60 (2023): 102147, https://doi.org/10.1016/j.phycom.2023.102147.
Z. Guo, Y. Niu, S. Mao, et al., “Sum Rate Maximization Under AoI Constraints for RIS-Assisted mmWave Communications,” IEEE Transactions on Vehicular Technology 73, no. 4 (2024): 5243–5258.
P. Zhang, S. Gong, and S. Ma, “Double-RIS Aided Multi-User MIMO Communications: Common Reflection Pattern and Joint Beamforming Design,” IEEE Transactions on Vehicular Technology 73, no. 3 (2024): 4418–4423.
H. Xu, C. Ouyang, and H. Yang, “RIS-Assisted mmWave MIMO Communications With Finite-Alphabet Inputs and Partial CSI,” IEEE Wireless Communications Letters 13, no. 5 (2024): 1339–1343.
D. Yang, J. Xu, W. Xu, et al., “Spatially Correlated RIS-Aided Secure Massive MIMO Under CSI and Hardware Imperfections,” IEEE Transactions on Wireless Communications 23, no. 9 (2024): 11461–11475.
H. Choi, A. L. Swindlehurst, and J. Choi, “WMMSE-Based Rate Maximization for RIS-Assisted MU-MIMO Systems,” IEEE Transactions on Communications 72, no. 8 (2024): 5194–5208.
M. Hassan, K. Hamid, H. M. Mujlid, E. Hassan, R. A. Saeed, and H. Elshafie, “Capacity and Power Allocation Optimization in Dynamic Ris-Enabled Mmimo Noma Systems for 6g,” Journal of Electrical Engineering 76, no. 1 (2025): 80–90, https://doi.org/10.2478/jee-2025-0008.
N. Moosavi, A. Zappone, P. Azmi, and M. Sinaie, “Delay-Aware and Energy-Efficient Resource Allocation for Reconfigurable Intelligent Surfaces,” IEEE Communications Letters 27, no. 2 (2023): 605–609.
U. M. R. Ukyam, P. Sireesha, P. D. P. Kumar, A. P. N. V. R. M. Ashish, and K. K. Gurrala, “Enhanced Physical Layer Security for RIS Aided Wireless Network With Control Jammer and Power Allocation Scheme,” in 2023 2nd International Conference on Paradigm Shifts in Communications Embedded Systems, Machine Learning and Signal Processing (PCEMS) (IEEE, 2023), 1–5.
Y. Li, Y. Zou, H. Guo, J. Zhu, and B. Ning, “Joint Power Allocation and Passive Beamforming for RIS-Assisted Wireless Energy-Constrained Systems With Multi-User Scheduling,” IEEE Transactions on Vehicular Technology 72, no. 11 (2023): 15109–15114.
Y. Cai, W. Yu, X. Nie, Q. Cheng, and T. Cui, “Joint Resource Allocation for RIS-Assisted Heterogeneous Networks With Centralized and Distributed Frameworks,” IEEE Transactions on Circuits and Systems I: Regular Papers 71, no. 5 (2024): 2132–2145.
Y. Liu, W. Chen, H. Tang, and K. Wang, “Resource Allocation in the RIS Assisted SCMA Cellular Network Coexisting With D2D Communications,” IEEE Access 11 (2023): 39978–39989.
K. Xie, G. Cai, G. Kaddoum, and J. He, “Performance Analysis and Resource Allocation of STAR-RIS-Aided Wireless-Powered NOMA System,” IEEE Transactions on Communications 71, no. 10 (2023): 5740–5755.
K. Lin, X. Mu, L. Guo, and A. Huang, “Resource Allocation for Integrated STAR-RISs and Full-Duplex Relay Communication Systems,” in ICC 2023—IEEE International Conference on Communications (IEEE, 2023), 2710–2715.
S. Ao, Y. Niu, Z. Han, et al., “Resource Allocation for RIS-Assisted Device-To-Device Communications in Heterogeneous Cellular Networks,” IEEE Transactions on Vehicular Technology 72, no. 9 (2023): 11741–11755.
J. Chen, S. Wang, J. Jia, Q. Wang, L. Yang, and X. Wang, “Multi-Objective Oriented Resource Allocation in Reconfigurable Intelligent Surface Assisted HCNs,” Ad Hoc Networks 140 (2023): 103066, https://doi.org/10.1016/j.adhoc.2022.103066.
Y. Qiao, Y. Niu, Z. Han, et al., “Joint Optimization of Resource Allocation and User Association in Multi-Frequency Cellular Networks Assisted by RIS,” IEEE Transactions on Vehicular Technology 73, no. 1 (2024): 826–842.
Y. Wang, J. Niu, G. Chen, X. Zhou, Y. Li, and S. Liu, “RIS-Aided Latency-Efficient MEC HetNet With Wireless Backhaul,” IEEE Transactions on Vehicular Technology 73, no. 6 (2024): 8705–8719.
H. Zhang, W. Wang, H. Zhou, Z. Lu, and M. Li, “A Hierarchical Drl Approach for Resource Optimization in Multi-Ris Multi-Operator Networks,” IEEE Transactions on Wireless Communications (2025): 1, https://doi.org/10.1109/TWC.2025.3545425.
M. Chemingui, A. Elzanaty, and R. Tafazolli, “Emf-Efficient Mu-Mimo Networks: Harnessing Aerial Ris Technology,” IEEE Transactions on Green Communications and Networking (2025): 1, https://doi.org/10.1109/TGCN.2025.3530011.
W. Ni, Y. Liu, Y. C. Eldar, Z. Yang, and H. Tian, “STAR-RIS Integrated Nonorthogonal Multiple Access and Over-The-Air Federated Learning: Framework, Analysis, and Optimization,” IEEE Internet of Things Journal 9, no. 18 (2022): 17136–17156.
S. Yang, J. Zhang, W. Xia, H. Gao, and H. Zhu, “Joint Power and Discrete Amplitude Allocation for STAR-RIS-Aided NOMA System,” IEEE Transactions on Vehicular Technology 71, no. 12 (2022): 13382–13386.
C. Wu, X. Mu, Y. Liu, X. Gu, and X. Wang, “Resource Allocation in STAR-RIS-Aided Networks: OMA and NOMA,” IEEE Transactions on Wireless Communications 21, no. 9 (2022): 7653–7667.
W. Xu, J. Chen, and X. Yu, “Joint Design of Power Allocation and Amplitude Coefficients for Ergodic Rate Optimization in STAR-RIS-Aided NOMA System,” IEEE Systems Journal 17, no. 4 (2023): 5452–5463.
R. Deshpande, M. V. Katwe, K. Singh, and Z. Ding, “Resource Allocation Design for Spectral-Efficient URLLC Using RIS-Aided FD-NOMA System,” IEEE Wireless Communications Letters 12, no. 7 (2023): 1209–1213.
Z. Wang and Q. Cao, “Resource Allocation of Backscatter Communication Based on Reconfigurable Intelligent Surface,” Advanced Control for Applications 6, no. 2 (2024): e146, https://doi.org/10.1002/adc2.146.
Q. Zhang, Y. Wang, H. Li, S. Hou, and Z. Song, “Resource Allocation for Energy Efficient STAR-RIS Aided MEC Systems,” IEEE Wireless Communications Letters 12, no. 4 (2023): 610–614.
M. Forouzanmehr and S. Akhlaghi, “Sum-Rate Maximization for RIS-Aided Full-Duplex Non-Orthogonal Multiple Access Networks: Joint Passive Beamforming and Resource Allocation,” IEEE Transactions on Green Communications and Networking 8, no. 1 (2024): 303–316.
X. Yu, G. Wang, X. Huang, K. Wang, W. Xu, and Y. Rui, “Energy Efficient Resource Allocation for Uplink RIS-Aided Millimeter-Wave Networks With NOMA,” IEEE Transactions on Mobile Computing 23, no. 1 (2024): 423–436.
J. Lei, T. Zhang, and Y. Liu, “Hybrid NOMA for STAR-RIS Enhanced Communication,” IEEE Transactions on Vehicular Technology 73, no. 1 (2024): 1497–1502.
W. Lyu, Y. Xiu, X. Li, et al., “Hybrid NOMA Assisted Integrated Sensing and Communication via RIS,” IEEE Transactions on Vehicular Technology 73, no. 5 (2024): 7368–7373.
G. Sun, Y. Zhang, W. Hao, Z. Zhu, X. Li, and Z. Chu, “Joint Beamforming Optimization for STAR-RIS Aided NOMA ISAC Systems,” IEEE Wireless Communications Letters 13, no. 4 (2024): 1009–1013.
X. Xu, W. Wu, and R. Song, “Transmit Power Minimization for RIS-Assisted MIMO-NOMA System,” IEEE Communications Letters 28, no. 6 (2024): 1367–1371.
S. Yang, J. Chen, J. Jia, L. Guo, and X. Wang, “Joint Resource Allocation and Blocklength Assignment in Star-Ris and Noma-Assisted Urllc Systems,” Computer Networks 257 (2025): 111001, https://doi.org/10.1016/j.comnet.2024.111001.
L. Ren, H. Zhang, Y. Zhu, and K. Long, “Resource Management for Intelligent Reflecting Surface Assisted THz-MIMO Network,” in 2021 IEEE Global Communications Conference (GLOBECOM) (IEEE, 2021), 1–5.
J. Qiao, C. Zhang, A. Dong, J. Bian, and M.-S. Alouini, “Securing Intelligent Reflecting Surface Assisted Terahertz Systems,” IEEE Transactions on Vehicular Technology 71, no. 8 (2022): 8519–8533.
W. Yan, W. Hao, C. Huang, et al., “Beamforming Analysis and Design for Wideband THz Reconfigurable Intelligent Surface Communications,” IEEE Journal on Selected Areas in Communications 41, no. 8 (2023): 2306–2320.
J. Praia, J. P. Pavia, N. Souto, and M. Ribeiro, “Phase Shift Optimization Algorithm for Achievable Rate Maximization in Reconfigurable Intelligent Surface-Assisted THz Communications,” Electronics 11, no. 1 (2021): 18.
Y. Ma, K. Ota, and M. Dong, “QoE Optimization for Virtual Reality Services in Multi-RIS-Assisted Terahertz Wireless Networks,” IEEE Journal on Selected Areas in Communications 42, no. 3 (2024): 538–551.
M. Shahwar, M. Ahmed, T. Hussain, et al., “Terahertz-Based Irs-Assisted Secure Symbiotic Radio Communication: A Drl Approach,” IEEE Access 13 (2025): 24014–24027.
Y. Chen, Y. Wang, J. Zhang, and Z. Li, “Resource Allocation for Intelligent Reflecting Surface Aided Vehicular Communications,” IEEE Transactions on Vehicular Technology 69, no. 10 (2020): 12321–12326.
A. A. Salem, M. Rihan, L. Huang, and A. Benaya, “Intelligent Reflecting Surface Assisted Hybrid Access Vehicular Communication: NOMA or OMA Contributes the Most?,” IEEE Internet of Things Journal 9, no. 19 (2022): 18854–18866.
Y. Chen, Y. Wang, Z. Wang, and P. Zhang, “Robust Beamforming for Active Reconfigurable Intelligent Omni-Surface in Vehicular Communications,” IEEE Journal on Selected Areas in Communications 40, no. 10 (2022): 3086–3103.
Y. Mao, X. Yang, L. Wang, et al., “A High-Capacity MAC Protocol for UAV-Enhanced RIS-Assisted V2X Architecture in 3D IoT Traffic,” IEEE Internet of Things Journal 11, no. 13 (2024): 23711–23726.
Y. Xie, L. Shi, Z. Li, X. Ding, and Y. Fan, “Efficient Task Offloading in Double Roadside Ris-Assisted Vehicular Edge Computing Networks Using Deep Reinforcement Learning,” IEEE Transactions on Vehicular Technology (2025): 1–13, https://doi.org/10.1109/TVT.2025.3543664.
Y. Yu, X. Liu, Z. Liu, and T. S. Durrani, “Joint Trajectory and Resource Optimization for RIS Assisted UAV Cognitive Radio,” IEEE Transactions on Vehicular Technology 72, no. 10 (2023): 13643–13648.
W. Feng, J. Tang, Q. Wu, et al., “Resource Allocation for Power Minimization in RIS-Assisted Multi-UAV Networks With NOMA,” IEEE Transactions on Communications 71, no. 11 (2023): 6662–6676.
X. Qin, Z. Song, T. Hou, W. Yu, J. Wang, and X. Sun, “Joint Optimization of Resource Allocation, Phase Shift, and UAV Trajectory for Energy-Efficient RIS-Assisted UAV-Enabled MEC Systems,” IEEE Transactions on Green Communications and Networking 7, no. 4 (2023): 1778–1792.
S. Liu, Y. Huang, H. Hu, et al., “Resource and Trajectory Optimization for Secure Communication in RIS Assisted UAV-MEC System,” IET Communications 17, no. 18 (2023): 2068–2079, https://doi.org/10.1049/cmu2.12680.
S. Li, H. Du, D. Zhang, and K. Li, “Joint UAV Trajectory and Beamforming Designs for RIS-Assisted MIMO System,” IEEE Transactions on Vehicular Technology 73, no. 4 (2024): 5378–5392.
N. Khan, A. Ahmad, A. Alwarafy, M. A. Shah, A. Lakas, and M. M. Azeem, “Efficient Resource Allocation and Uav Deployment in Star-Ris and Uav-Relay Assisted Public Safety Networks for Video Transmission,” IEEE Open Journal of the Communications Society 6 (2025): 1804–1820.
T. Bai, C. Pan, H. Ren, Y. Deng, M. Elkashlan, and A. Nallanathan, “Resource Allocation for Intelligent Reflecting Surface Aided Wireless Powered Mobile Edge Computing in OFDM Systems,” IEEE Transactions on Wireless Communications 20, no. 8 (2021): 5389–5407.
H. Zhang, X. He, Q. Wu, and H. Dai, “Spectral Graph Theory Based Resource Allocation for IRS-Assisted Multi-Hop Edge Computing,” in IEEE INFOCOM 2021—IEEE Conference on Computer Communications Workshops (INFOCOM WKSHPS) (IEEE, 2021), 1–6.
P. D. Lorenzo, M. Merluzzi, and E. C. Strinati, “Dynamic Mobile Edge Computing Empowered by Reconfigurable Intelligent Surfaces,” in 2021 IEEE 22nd International Workshop on Signal Processing Advances in Wireless Communications (SPAWC) (IEEE, 2021), 526–530.
P. Di Lorenzo, M. Merluzzi, E. Calvanese Strinati, and S. Barbarossa, “Dynamic Edge Computing Empowered by Reconfigurable Intelligent Surfaces,” EURASIP Journal on Wireless Communications and Networking 2022, no. 1 (2022): 122, https://doi.org/10.1186/s13638-022-02203-6.
Y. Zhu, B. Mao, and N. Kato, “A Dynamic Task Scheduling Strategy for Multi-Access Edge Computing in IRS-Aided Vehicular Networks,” IEEE Transactions on Emerging Topics in Computing 10, no. 4 (2022): 1761–1771.
B. Wang, R. Liu, Y. Li, C. Ding, J.-B. Wang, and H. Zhang, “Joint Optimization of Transmission and Computing Resource in IRS-Assisted Mobile Edge Computing System,” in 2022 IEEE Wireless Communications and Networking Conference (WCNC) (IEEE, 2022), 381–386.
W. He, D. He, X. Ma, X. Chen, Y. Fang, and W. Zhang, “Joint User Association, Resource Allocation, and Beamforming in RIS-Assisted Multi-Server MEC Systems,” IEEE Transactions on Wireless Communications 23, no. 4 (2024): 2917–2932.
G. Chen, Y. Chen, Z. Mai, et al., “Joint Multiple Resource Allocation for Offloading Cost Minimization in IRS-Assisted MEC Networks With NOMA,” Digital Communications and Networks 9, no. 3 (2023): 613–627, https://doi.org/10.1016/j.dcan.2022.10.029.
X. Qin, Z. Song, T. Hou, W. Yu, J. Wang, and X. Sun, “Joint Resource Allocation and Configuration Design for STAR-RIS-Enhanced Wireless-Powered MEC,” IEEE Transactions on Communications 71, no. 4 (2023): 2381–2395.
A. Huang, L. Qu, and M. J. Khabbaz, “Latency-Aware Computation Offloading in Multi-RIS-Assisted Edge Networks,” IEEE Open Journal of the Communications Society 5 (2024): 1204–1221.
Z. Yang, L. Xia, J. Cui, Z. Dong, and Z. Ding, “Delay and Energy Minimization for Cooperative NOMA-MEC Networks With SWIPT Aided by RIS,” IEEE Transactions on Vehicular Technology 73, no. 4 (2024): 5321–5334.
L. Liu, H. Wang, and R. Song, “Optimization for Multi-Cell NOMA Systems Assisted by Multi-RIS With Inter-RIS Reflection,” IEEE Communications Letters 28, no. 1 (2024): 123–127.
Y. Liao, L. Liu, and Y. Ma, “Energy- and Latency-Efficient Resource Allocation for Ris-Assisted Uav-Usv Cooperative Mec Network,” IEEE Transactions on Green Communications and Networking (2025): 1, https://doi.org/10.1109/TGCN.2025.3545458.
M. Eskandari, H. Zhu, A. Shojaeifard, and J. Wang, “Statistical CSI-Based Beamforming for RIS-Aided Multiuser MISO Systems via Deep Reinforcement Learning,” IEEE Wireless Communications Letters 13, no. 2 (2024): 570–574.
Y. Zhang and H. Xu, “Optimal Resource Allocation for Reconfigurable Intelligent Surface Assisted Dynamic Wireless Network via Online Reinforcement Learning,” in 2022 IEEE International Conference on Sensing, Communication, and Networking (SECON Workshops) (IEEE, 2022), 13–18.
P. Ji, J. Jia, J. Chen, L. Guo, A. Du, and X. Wang, “Reinforcement Learning Based Joint Trajectory Design and Resource Allocation for RIS-Aided UAV Multicast Networks,” Computer Networks 227 (2023): 109697, https://doi.org/10.1016/j.comnet.2023.109697.
K. Cao and Q. Tang, “Energy Efficiency Maximization for RIS-Assisted MISO Symbiotic Radio Systems Based on Deep Reinforcement Learning,” IEEE Communications Letters 28, no. 1 (2024): 88–92.
W. Xia, Y. Jiang, B. Zhao, H. Zhao, and H. Zhu, “Deep Unfolded Fractional Programming-Based Beamforming in RIS-Aided MISO Systems,” IEEE Wireless Communications Letters 13, no. 2 (2024): 515–519.
S. Kurma, M. Katwe, K. Singh, T. Q. Duong, and C.-P. Li, “Spectral-Energy Efficient Resource Allocation in RIS-Aided FD-MIMO Systems,” IEEE Transactions on Wireless Communications 23, no. 5 (2023): 5125–5141.
A. Adhikary, M. S. Munir, A. Deb Raha, et al., “An Artificial Intelligence Framework for Holographic Beamforming: Coexistence of Holographic MIMO and Intelligent Omni-Surface,” in 2023 International Conference on Information Networking (ICOIN) (IEEE, 2023), 19–24.
Y. Zhang, L. Qian, A. Eroglu, B. Yang, and H. Xu, “Reinforcement Learning Based Optimal Dynamic Resource Allocation for RIS-Aided MIMO Wireless Network With Hardware Limitations,” in 2023 International Conference on Computing, Networking and Communications (ICNC) (IEEE, 2023), 148–152.
P. Saikia, K. Singh, O. Taghizadeh, W.-J. Huang, and S. Biswas, “Meta Reinforcement Learning-Based Spectrum Sharing Between RIS-Assisted Cellular Communications and MIMO Radar,” IEEE Transactions on Cognitive Communications and Networking 10, no. 1 (2024): 164–179.
G. Sun, X. Wang, R. Jiang, T. Liu, and Y. Xu, “Bi-Directional Deep Transfer Learning for RIS-Enhanced Multi-Cell OFDMA Systems,” in 2022 14th International Conference on Wireless Communications and Signal Processing (WCSP) (IEEE, 2022), 154–159.
Q. Pan, J. Wu, J. Nebhen, A. K. Bashir, Y. Su, and J. Li, “Artificial Intelligence-Based Energy Efficient Communication System for Intelligent Reflecting Surface-Driven VANETs,” IEEE Transactions on Intelligent Transportation Systems 23, no. 10 (2022): 19714–19726.
L. Wang, F. Yang, Y. Chen, S. Lai, and W. Wu, “Intelligent Resource Allocation for Transmission Security on IRS-Assisted Spectrum Sharing Systems With OFDM,” Physical Communication 58 (2023): 102013, https://doi.org/10.1016/j.phycom.2023.102013.
Y. Zhang and H. Xu, “Two-Stage Online Reinforcement Learning Based Distributed Optimal Resource Allocation for Multiple RIS-Assisted Mobile Ad-Hoc Network,” in 2023 International Conference on Computing, Networking and Communications (ICNC) (IEEE, 2023), 563–567.
J. Mao and A. Yener, “ROAR-Fed: RIS-Assisted Over-The-Air Adaptive Resource Allocation for Federated Learning,” in ICC 2023—IEEE International Conference on Communications (IEEE, 2023), 4341–4346.
L. Guo, J. Jia, J. Chen, A. Du, and X. Wang, “Deep Reinforcement Learning Empowered Joint Mode Selection and Resource Allocation for RIS-Aided D2D Communications,” Neural Computing and Applications 35, no. 25 (2023): 18231–18249, https://doi.org/10.1007/s00521-023-08745-0.
H. Yang, K. Lin, L. Xiao, Y. Zhao, Z. Xiong, and Z. Han, “Energy Harvesting UAV-RIS-Assisted Maritime Communications Based on Deep Reinforcement Learning Against Jamming,” IEEE Transactions on Wireless Communications 23, no. 8 (2024): 9854–9868.
A. Huang, X. Mu, L. Guo, and G. Zhu, “Hybrid Active-Passive RIS Transmitter Enabled Energy-Efficient Multi-User Communications,” IEEE Transactions on Wireless Communications 23, no. 9 (2024): 10653–10666.
S. Mao, L. Liu, N. Zhang, et al., “Intelligent Reflecting Surface-Assisted Low-Latency Federated Learning Over Wireless Networks,” IEEE Internet of Things Journal 10, no. 2 (2023): 1223–1235.
L. Guo, J. Jia, Y. Zou, J. Chen, L. Yang, and X. Wang, “Resource Allocation for Multiple RISs Assisted NOMA Empowered D2D Communication: A MAMP-DQN Approach,” Ad Hoc Networks 146 (2023): 103163, https://doi.org/10.1016/j.adhoc.2023.103163.
D. Chen, H. Gao, N. Chen, and R. Cao, “Integrated Beamforming and Resource Allocation in RIS-Assisted mmWave Networks Based on Deep Reinforcement Learning,” in In 2023 21st IEEE Interregional NEWCAS Conference (NEWCAS) (IEEE, 2023), 1–5.
H. Zarini, N. Gholipoor, M. R. Mili, M. Rasti, H. Tabassum, and E. Hossain, “Resource Management for Multiplexing eMBB and URLLC Services Over RIS-Aided THz Communication,” IEEE Transactions on Communications 71, no. 2 (2023): 1207–1225.
M. Bensalem, A. Engelmann, and A. Jukan, “Towards Optimal Path Allocation for Unreliable Reconfigurable Intelligent Surfaces,” in 2023 19th International Conference on the Design of Reliable Communication Networks (DRCN) (IEEE, 2023), 1–8.
A. Mehrabian and V. W. Wong, “Joint Spectrum, Precoding, and Phase Shifts Design for RIS-Aided Multiuser MIMO THz Systems,” IEEE Transactions on Communications 72, no. 8 (2024): 5087–5101.
P. S. Aung, L. X. Nguyen, Y. K. Tun, Z. Han, and C. S. Hong, “Deep Reinforcement Learning-Based Joint Spectrum Allocation and Configuration Design for STAR-RIS-Assisted V2X Communications,” IEEE Internet of Things Journal 11, no. 7 (2024): 11298–11311.
Y. Zhang, L. Qian, and H. Xu, “Joint Optimal Placement and Dynamic Resource Allocation for Multi-UAV Enhanced Reconfigurable Intelligent Surface Assisted Wireless Network,” in 2023 IEEE 20th Consumer Communications & Networking Conference (CCNC), Las Vegas (IEEE, 2023), 1–6.
P. S. Aung, L. X. Nguyen, Y. K. Tun, Z. Han, and C. S. Hong, “Aerial STAR-RIS Empowered MEC: A DRL Approach for Energy Minimization,” IEEE Wireless Communications Letters 13, no. 5 (2024): 1409–1413.
Y. Liu, C. Huang, G. Chen, R. Song, S. Song, and P. Xiao, “Deep Learning Empowered Trajectory and Passive Beamforming Design in UAV-RIS Enabled Secure Cognitive Non-Terrestrial Networks,” IEEE Wireless Communications Letters 13, no. 1 (2024): 188–192.
B. Yang, X. Cao, C. Huang, et al., “Federated Spectrum Learning for Reconfigurable Intelligent Surfaces-Aided Wireless Edge Networks,” IEEE Transactions on Wireless Communications 21, no. 11 (2022): 9610–9626.
Z. Wang, Y. Wei, Z. Feng, F. R. Yu, and Z. Han, “Resource Management and Reflection Optimization for Intelligent Reflecting Surface Assisted Multi-Access Edge Computing Using Deep Reinforcement Learning,” IEEE Transactions on Wireless Communications 22, no. 2 (2023): 1175–1186.
S. Zhuang, Y. He, F. R. Yu, C. Gao, W. Pan, and Z. Ming, “When Multi-Access Edge Computing Meets Multi-Area Intelligent Reflecting Surface: A Multi-Agent Reinforcement Learning Approach,” in 2022 IEEE/ACM 30th International Symposium on Quality of Service (IWQoS) (IEEE, 2022), 1–10.
Y. Cheng, J. Lu, D. Niyato, B. Lyu, M. Xu, and S. Zhu, “Performance Analysis and Power Allocation for Covert Mobile Edge Computing With RIS-Aided NOMA,” IEEE Transactions on Mobile Computing 23, no. 5 (2024): 4212–4227.
X. Zhang, W. Wu, S. Liu, and J. Wang, “An Efficient Computation Offloading and Resource Allocation Algorithm in RIS Empowered MEC,” Computer Communications 197 (2023): 113–123, https://doi.org/10.1016/j.comcom.2022.10.012.
Q. Liu, J. Zhu, and Q. Liu, “Joint Computation Offloading and Power Allocation Strategy in NOMA-Based Dynamic MEC Network Assisted by RIS,” in 2023 IEEE 34th Annual International Symposium on Personal, Indoor and Mobile Radio Communications (PIMRC) (IEEE, 2023), 1–7.
Q. Liu, J. Han, and Q. Liu, “Joint Task Offloading and Resource Allocation for RIS-Assisted UAV for Mobile Edge Computing Networks,” in 2023 IEEE/CIC International Conference on Communications in China (ICCC) (IEEE, 2023), 1–6.
K. Stylianopoulos, M. Merluzzi, P. Di Lorenzo, and G. C. Alexandropoulos, “Lyapunov-Driven Deep Reinforcement Learning for Edge Inference Empowered by Reconfigurable Intelligent Surfaces,” in ICASSP 2023–2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) (IEEE, 2023), 1–5.
J. Chen, J. Xia, J. Jia, L. Yang, and X. Wang, “Cooperative Caching, Rendering, and Beamforming for RIS-Assisted Wireless Virtual Reality Networks,” IEEE Transactions on Vehicular Technology 73, no. 5 (2024): 6845–6860.
J. Xu, A. Xu, L. Chen, Y. Chen, X. Liang, and B. Ai, “Deep Reinforcement Learning for RIS-Aided Secure Mobile Edge Computing in Industrial Internet of Things,” IEEE Transactions on Industrial Informatics 20, no. 2 (2024): 2455–2464.
S. Javadi, A. Farhadi, M. Robat Mili, E. Jorswieck, and N. Al-Dhahir, “Meta-Learning for Resource Allocation in Uplink Multi-Active Star-Ris-Aided Noma System,” IEEE Wireless Communications Letters 14, no. 3 (2025): 781–785.
A. Jabbar, M. A. Jamshed, Q. Abbasi, M. A. Imran, and M. Ur-Rehman, “Leveraging the Role of Dynamic Reconfigurable Antennas in Viewpoint of Industry 4.0 and Beyond,” Research 6 (2023): 110.
K. Rasilainen, T. D. Phan, M. Berg, A. Pärssinen, and P. J. Soh, “Hardware Aspects of Sub-THz Antennas and Reconfigurable Intelligent Surfaces for 6G Communications,” IEEE Journal on Selected Areas in Communications 41, no. 8 (2023): 2530–2546.
T. Ma, Y. Xiao, X. Lei, L. Zhang, Y. Niu, and G. K. Karagiannidis, “Reconfigurable Intelligent Surface-Assisted Localization: Technologies, Challenges, and the Road Ahead,” IEEE Open Journal of the Communications Society 4 (2023): 1430–1451.
A. K. Singh, A. K. Maurya, R. Prakash, P. Thakur, and B. B. Tiwari, “Reconfigurable Intelligent Surface With 6G for Industrial Revolution: Potential Applications and Research Challenges,” Paladyn, Journal of Behavioral Robotics 14, no. 1 (2023): 20220114, https://doi.org/10.1515/pjbr-2022-0114.
R. Singh, A. Kaushik, W. Shin, G. C. Alexandropoulos, M. Toka, and M. Di Renzo, “Indexed Multiple Access With Reconfigurable Intelligent Surfaces: The Reflection Tuning Potential,” IEEE Communications Magazine 62, no. 4 (2024): 120–126.
F. Naeem, M. Ali, G. Kaddoum, C. Huang, and C. Yuen, “Security and Privacy for Reconfigurable Intelligent Surface in 6G: A Review of Prospective Applications and Challenges,” IEEE Open Journal of the Communications Society 4 (2023): 1196–1217.
R. Liu, M. Li, H. Luo, Q. Liu, and A. L. Swindlehurst, “Integrated Sensing and Communication With Reconfigurable Intelligent Surfaces: Opportunities, Applications, and Future Directions,” IEEE Wireless Communications 30, no. 1 (2023): 50–57.
F. A. P. de Figueiredo, “Unlocking the Power of Reconfigurable Intelligent Surfaces: From Wireless Communication to Energy Efficiency and Beyond,” Applied Sciences 13, no. 21 (2023): 11750, https://doi.org/10.3390/app132111750.
J. Xu, C. Yuen, C. Huang, et al., “Reconfiguring Wireless Environments via Intelligent Surfaces for 6G: Reflection, Modulation, and Security,” Science China Information Sciences 66, no. 3 (2023): 130304, https://doi.org/10.1007/s11432-022-3626-5.
K. T. Selvan and R. Janaswamy, “Fraunhofer and Fresnel Distances: Unified Derivation for Aperture Antennas,” IEEE Antennas and Propagation Magazine 59, no. 4 (2017): 12–15.