The DOI matches its Crossref record; 192 of the 193 references it cites were found in a registry or an index.
Toward Smart Wireless Communications via Intelligent Reflecting Surfaces: A Contemporary Survey
Open accessAbstract
This paper presents a literature review on recent applications and design aspects of the intelligent reflecting surface (IRS) in the future wireless networks. Conventionally, the network optimization has been limited to transmission control at two endpoints, i.e., end users and network controller. The fading wireless channel is uncontrollable and becomes one of the main limiting factors for performance improvement. The IRS is composed of a large array of scattering elements, which can be individually configured to generate additional phase shifts to the signal reflections. Hence, it can actively control the signal propagation properties in favor of signal reception, and thus realize the notion of a smart radio environment. As such, the IRS's phase control, combined with the conventional transmission control, can potentially bring performance gain compared to wireless networks without IRS. In this survey, we first introduce basic concepts of the IRS and the realizations of its reconfigurability. Then, we focus on applications of the IRS in wireless communications. We overview different performance metrics and analytical approaches to characterize the performance improvement of IRS-assisted wireless networks. To exploit the performance gain, we discuss the joint optimization of the IRS's phase control and the transceivers' transmission control in different network design problems, e.g., rate maximization and power minimization problems. Furthermore, we extend the discussion of IRS-assisted wireless networks to some emerging use cases. Finally, we highlight important practical challenges and future research directions for realizing IRS-assisted wireless networks in beyond 5G communications.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
The source backs part of the claim, not all of it — or backs it for a different population, condition or comparison.
In the citing paper
“By adequately training and configuring the transmission coefficients of the meta-atoms using error backpropagation, a SIM is capable of performing desired functions through the interactions of spatial EM waves with the metasurface layers [15], [16].”
From Emerging Technologies in Intelligent Metasurfaces: Shaping the Future of Wireless Communications (2026) · cites this paper as [16]
Experimental — accuracy not yet measuredThe source backs part of the claim, not all of it — or backs it for a different population, condition or comparison.
In the citing paper
“In HOENN, the power-efficient SIM can preprocess spatial EM waves, reducing the scale requirements and the computation load for the electronic neuron network [16].”
From Emerging Technologies in Intelligent Metasurfaces: Shaping the Future of Wireless Communications (2026) · cites this paper as [16]
Experimental — accuracy not yet measured
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
Light Propagation with Phase Discontinuities: Generalized Laws of Reflection and Refraction
Yu, N., Genevet, P., Kats, M. A., Aieta, F., Tetienne, J.-P., Capasso, F., & Gaburro, Z. (2011) · Science · doi:10.1126/science.1210713 · Cited by 10,361
Intelligent Reflecting Surface Enhanced Wireless Network via Joint Active and Passive Beamforming
Wu, Q., & Zhang, R. (2019) · IEEE Transactions on Wireless Communications · doi:10.1109/twc.2019.2936025 · Cited by 4,545
Towards Smart and Reconfigurable Environment: Intelligent Reflecting Surface Aided Wireless Network
Wu, Q., & Zhang, R. (2019) · IEEE Communications Magazine · doi:10.1109/mcom.001.1900107 · Cited by 4,296
Reconfigurable Intelligent Surfaces for Energy Efficiency in Wireless Communication
Huang, C., Zappone, A., Alexandropoulos, G. C., Debbah, M. A., & Yuen, C. (2019) · IEEE Transactions on Wireless Communications · doi:10.1109/twc.2019.2922609 · Cited by 3,774
Wireless Communications Through Reconfigurable Intelligent Surfaces
Basar, E., Di Renzo, M., De Rosny, J., Debbah, M., Alouini, M.-S., & Zhang, R. (2019) · IEEE Access · doi:10.1109/access.2019.2935192 · Cited by 3,394
A review of metasurfaces: physics and applications
Chen, H., Taylor, A. J., & Yu, N. (2016) · Reports on Progress in Physics · doi:10.1088/0034-4885/79/7/076401 · Cited by 2,618
Holloway, C. L., Kuester, E. F., Gordon, J. A., O’Hara, J. F., Booth, J. C., & Smith, D. R. (2012) · IEEE Antennas and Propagation Magazine · doi:10.1109/map.2012.6230714 · Cited by 2,369
Applications of Deep Reinforcement Learning in Communications and Networking: A Survey
Luong, N. C., Hoang, D. T., Gong, S., Niyato, D. T., Wang, P., Liang, Y., & Kim, D. I. (2019) · IEEE Communications Surveys & Tutorials · doi:10.1109/comst.2019.2916583 · Cited by 2,181
Self-citationDeep Learning in Mobile and Wireless Networking: A Survey
Zhang, C., Patras, P., & Haddadi, H. (2019) · IEEE Communications Surveys & Tutorials · doi:10.1109/comst.2019.2904897 · Cited by 1,913
Smart radio environments empowered by reconfigurable AI meta-surfaces: an idea whose time has come
Renzo, M. D., Debbah, M., Phan-Huy, D.-T., Zappone, A., Alouini, M.-S., Yuen, C., Sciancalepore, V., Alexandropoulos, G. C., Hoydis, J., Gacanin, H., et al. (2019) · EURASIP Journal on Wireless Communications and Networking · doi:10.1186/s13638-019-1438-9 · Cited by 1,717
Flagged first, then by citations
1 of the 193 references couldn’t be matched to a known paper.
Research works printed but not linked: 10
- lu, 2020, Intelligent Reflecting Surface (IRS)-Enabled Covert Communications in Wireless Networks
- 10.1109/GLOBECOM38437.2019.9013626 · DOI
- wu, 2019, Joint active and passive beamforming optimization for intelligent reflecting surface assisted SWIPT under QOS constraints
- zhang, 2020, Large Intelligent Surface/Antennas (LISA) Assisted Symbiotic Radio for IoT Communications
- li, 2020, Joint beamforming design in multi-cluster MISO NOMA intelligent reflecting surface-aided downlink communication networks
- 2020, A Hardware Platform for Software-Driven Functional Metasurfaces
- tang, 2019, Wireless communications with reconfigurable intelligent surface Path loss modeling and experimental measurement
- abeywickrama, 2019, Intelligent reflecting surface Practical phase shift model and beamforming optimization
- huang, 2019, Holographic MIMO surfaces for 6G wireless networks Opportunities chanllenges and trends
- wang, 2019, Intelligent reflecting surface-assisted millimeter wave communications Joint active and passive precoding design
Cited by
Sorted by citations
Items 101–200 of 1,001
Chen, W., Bai, L., Tang, W., Jin, S., Jiang, W., & Cui, T. J. (2020) · IEEE Communications Letters · doi:10.1109/lcomm.2020.2993961 · Cited by 86
Reconfigurable Intelligent Surface Enhanced Multi-User MISO Symbiotic Radio System
Hu, J., Liang, Y., & Pei, Y. (2020) · IEEE Transactions on Communications · doi:10.1109/tcomm.2020.3047444 · Cited by 86
Self-citationHybrid Reinforcement Learning for STAR-RISs: A Coupled Phase-Shift Model Based Beamformer
Zhong, R., Liu, Y., Mu, X., Chen, Y., Wang, X., & Hanzo, L. (2022) · IEEE Journal on Selected Areas in Communications · doi:10.1109/jsac.2022.3192053 · Cited by 91
Intelligent Reflecting Surface Aided Multiple Access Over Fading Channels
Guo, Y., Qin, Z., Liu, Y., & Al‐Dhahir, N. (2020) · IEEE Transactions on Communications · doi:10.1109/tcomm.2020.3042277 · Cited by 84
A Survey of 6G Wireless Communications: Emerging Technologies
Zhao, Y., Zhao, J., Zhai, W., Sun, S., Niyato, D. T., & Lam, K. (2021) · Advances in intelligent systems and computing · doi:10.1007/978-3-030-73100-7_12 · Cited by 84
Self-citation5G NR system design: a concise survey of key features and capabilities
Rinaldi, F., Raschellà, A., & Pizzi, S. (2021) · Wireless Networks · doi:10.1007/s11276-021-02811-y · Cited by 84
Optimal Power Allocation in Downlink Multicarrier NOMA Systems: Theory and Fast Algorithms
Rezvani, S., Jorswieck, E. A., Joda, R., & Yanıkömeroğlu, H. (2022) · IEEE Journal on Selected Areas in Communications · doi:10.1109/jsac.2022.3143237 · Cited by 84
Machine Learning for User Partitioning and Phase Shifters Design in RIS-Aided NOMA Networks
Yang, Z., Liu, Y., Chen, Y., & Al‐Dhahir, N. (2021) · IEEE Transactions on Communications · doi:10.1109/tcomm.2021.3100866 · Cited by 83
Sensing-Based Beamforming Design for Joint Performance Enhancement of RIS-Aided ISAC Systems
Qian, X., Hu, X., Liu, C., Peng, M., & Zhong, C. (2023) · IEEE Transactions on Communications · doi:10.1109/tcomm.2023.3299980 · Cited by 80
Security in Energy Harvesting Networks: A Survey of Current Solutions and Research Challenges
Tedeschi, P., Sciancalepore, S., & Pietro, R. D. (2020) · IEEE Communications Surveys & Tutorials · doi:10.1109/comst.2020.3017665 · Cited by 79
Wang, J., Tang, W., Han, Y., Jin, S., Li, X., Wen, C.-K., Cheng, Q., & Cui, T. J. (2021) · IEEE Journal on Selected Areas in Communications · doi:10.1109/jsac.2021.3087259 · Cited by 88
Two-Stage Channel Estimation for Hybrid RIS Assisted MIMO Systems
Schroeder, R., He, J., Brante, G. G. D. O., & Juntti, M. J. (2022) · IEEE Transactions on Communications · doi:10.1109/tcomm.2022.3176654 · Cited by 77
Enabling Efficient Blockage-Aware Handover in RIS-Assisted mmWave Cellular Networks
Jiao, L., Wang, P., Alipour-Fanid, A., Zeng, H., & Zeng, K. (2021) · IEEE Transactions on Wireless Communications · doi:10.1109/twc.2021.3110522 · Cited by 75
Elhattab, M., Arfaoui, M. A., Assi, C. M., & Ghrayeb, A. (2021) · IEEE Transactions on Wireless Communications · doi:10.1109/twc.2021.3120989 · Cited by 73
Random Shifting Intelligent Reflecting Surface for OTP Encrypted Data Transmission
Ji, Z., Yeoh, P. L., Chen, G., Pan, C., Zhang, Y., He, Z., Yin, H., & Li, Y. (2021) · IEEE Wireless Communications Letters · doi:10.1109/lwc.2021.3061549 · Cited by 73
Resource Allocation for IRS Assisted SGF NOMA Transmission: A MADRL Approach
Chen, J., Guo, L., Jia, J., Shang, J., & Wang, X. (2022) · IEEE Journal on Selected Areas in Communications · doi:10.1109/jsac.2022.3144726 · Cited by 73
Robust Beamformer Design in Active RIS-Assisted Multiuser MIMO Cognitive Radio Networks
Allu, R., Taghizadeh, O., Singh, S. K., Singh, K. K., & Li, C. (2023) · IEEE Transactions on Cognitive Communications and Networking · doi:10.1109/tccn.2023.3235788 · Cited by 73
Emerging Technologies in Intelligent Metasurfaces: Shaping the Future of Wireless Communications
An, J., Debbah, M., Cui, T. J., Chen, Z. N., & Yuen, C. (2026) · IEEE Transactions on Antennas and Propagation · doi:10.1109/tap.2025.3571069 · Cited by 72
“While several recent survey papers [8], [12], [14], [15], [16], [17], [18], [32] have examined the same topic, they have primarily focused on 2-”
Munochiveyi, M. B., Pogaku, A. C., Do, D., Le, A., Vozn̆ák, M., & Nguyen, N. D. (2021) · IEEE Access · doi:10.1109/access.2021.3107316 · Cited by 71
A survey on UAV placement optimization for UAV-assisted communication in 5G and beyond networks
Elnabty, I. A., Fahmy, Y. A. H., & Kafafy, M. B. S. A. (2021) · Physical Communication · doi:10.1016/j.phycom.2021.101564 · Cited by 71
Sarkar, D., Yogita, Y., Yadav, S. S., Pal, V., Kumar, N., & Patra, S. K. (2024) · IEEE Transactions on Network and Service Management · doi:10.1109/tnsm.2023.3348138 · Cited by 71
Secure Transmission Design for Aerial IRS Assisted Wireless Networks
Wei, W., Pang, X., Tang, J., Zhao, N., Wang, X., & Nallanathan, A. (2023) · IEEE Transactions on Communications · doi:10.1109/tcomm.2023.3257387 · Cited by 70
Reconfigurable Intelligent Surface-Empowered MIMO Systems
Khaleel, A., & Başar, E. (2020) · IEEE Systems Journal · doi:10.1109/jsyst.2020.3011987 · Cited by 70
Robust Secure Transmission for Active RIS Enabled Symbiotic Radio Multicast Communications
Lyu, B., Zhou, C., Gong, S., Hoang, D. T., & Liang, Y. (2023) · IEEE Transactions on Wireless Communications · doi:10.1109/twc.2023.3265770 · Cited by 69
Self-citationPan, Y., Wang, K., Pan, C., Zhu, H., & Wang, J. (2022) · IEEE Transactions on Wireless Communications · doi:10.1109/twc.2022.3140268 · Cited by 68
Link Budget Analysis for Reconfigurable Smart Surfaces in Aerial Platforms
Alfattani, S., Jaafar, W., Hmamouche, Y., Yanıkömeroğlu, H., & Yongaçoğlu, A. (2021) · IEEE Open Journal of the Communications Society · doi:10.1109/ojcoms.2021.3105933 · Cited by 68
Reconfigurable Intelligent Surface Assisted Spatial Modulation for Symbiotic Radio
Wu, M., Lei, X., Zhou, X., Xiao, Y., Tang, X., & Hu, R. Q. (2021) · IEEE Transactions on Vehicular Technology · doi:10.1109/tvt.2021.3121698 · Cited by 68
Channel Estimation for Reconfigurable Intelligent Surface Assisted High-Mobility Wireless Systems
Xu, C., An, J., Bai, T., Sugiura, S., Maunder, R. G., Wang, Z., Yang, L., & Hanzo, L. (2022) · IEEE Transactions on Vehicular Technology · doi:10.1109/tvt.2022.3203818 · Cited by 68
Sun, S., Mei, W., Yang, F. C., An, N., Song, J., & Zhang, R. (2023) · IEEE Transactions on Wireless Communications · doi:10.1109/twc.2023.3295793 · Cited by 68
The Prospect of Reconfigurable Intelligent Surfaces in Integrated Access and Backhaul Networks
Diamanti, M., Charatsaris, P., Tsiropoulou, E. E., & Papavassiliou, S. (2021) · IEEE Transactions on Green Communications and Networking · doi:10.1109/tgcn.2021.3126784 · Cited by 67
Eteng, A. A., Goh, H. H., Rahim, S. K. A., & Alomainy, A. (2021) · IEEE Access · doi:10.1109/access.2021.3058151 · Cited by 66
Energy-Efficient Maximization for RIS-Aided MISO Symbiotic Radio Systems
Zhou, C., Xu, Y., Li, D., Huang, C., Yuen, C., Zhou, J., & Yang, G. (2023) · IEEE Transactions on Vehicular Technology · doi:10.1109/tvt.2023.3274796 · Cited by 66
Power Measurement-Based Channel Estimation for IRS-Enhanced Wireless Coverage
Sun, H., Zhu, L., Mei, W., & Zhang, R. (2024) · IEEE Transactions on Wireless Communications · doi:10.1109/twc.2024.3480314 · Cited by 79
Cao, Y., Lv, T., Ni, W., & Lin, Z. (2021) · IEEE Transactions on Communications · doi:10.1109/tcomm.2021.3106334 · Cited by 66
Covert Beamforming Design for Intelligent-Reflecting-Surface-Assisted IoT Networks
Ma, S., Zhang, Y., Li, H., Sun, J., Shi, J. D., Zhang, H., Shen, C., & Li, S. (2021) · IEEE Internet of Things Journal · doi:10.1109/jiot.2021.3109746 · Cited by 65
Ai, Q., Qiao, X., Liao, Y., & Yu, Q. G. (2021) · IEEE Transactions on Green Communications and Networking · doi:10.1109/tgcn.2021.3135530 · Cited by 64
Software-Defined Reconfigurable Intelligent Surfaces: From Theory to End-to-End Implementation
Liaskos, C., Mamatas, L., Pourdamghani, A., Tsioliaridou, A., Ioannidis, S., Pitsillides, A., Schmid, S., & Akyildiz, I. F. (2022) · Proceedings of the IEEE · doi:10.1109/jproc.2022.3169917 · Cited by 69
Reliable Communications for Cybertwin-Driven 6G IoVs Using Intelligent Reflecting Surfaces
Javed, M. A., Nguyen, T. N., Mirza, J., Ahmed, J., & Ali, B. Q. (2022) · IEEE Transactions on Industrial Informatics · doi:10.1109/tii.2022.3151773 · Cited by 64
Reconfigurable Intelligent Surface Aided Power Control for Physical-Layer Broadcasting
Han, H., Zhao, J., Zhai, W., Xiong, Z., Niyato, D. T., Renzo, M. D., Pham, Q., Lu, W., & Lam, K. (2021) · IEEE Transactions on Communications · doi:10.1109/tcomm.2021.3104871 · Cited by 63
Self-citationCao, X., Yang, B. F., Huang, C., Yuen, C., Renzo, M. D., Han, Z., Niyato, D. T., Poor, H. V., & Hanzo, L. (2021) · IEEE Communications Magazine · doi:10.1109/mcom.001.2001146 · Cited by 62
Self-citationGraph-Embedded Multi-Agent Learning for Smart Reconfigurable THz MIMO-NOMA Networks
Xu, X., Chen, Q., Mu, X., Liu, Y., & Jiang, H. (2021) · IEEE Journal on Selected Areas in Communications · doi:10.1109/jsac.2021.3126079 · Cited by 61
A Survey on Applications of Cache-Aided NOMA
Bepari, D., Mondal, S., Chandra, A., Shukla, R., Liu, Y., Guizani, M., & Nallanathan, A. (2023) · IEEE Communications Surveys & Tutorials · doi:10.1109/comst.2023.3293231 · Cited by 61
A Survey on RIS Advances in Terahertz Communications: Emerging Paradigms and Research Frontiers
Ahmed, M., Wahid, A., Khan, W. U., Khan, F., Ihsan, A., Ali, Z., Rabie, K. M., Shongwe, T., & Han, Z. (2024) · IEEE Access · doi:10.1109/access.2024.3482564 · Cited by 63
Joint Beamforming and Reconfigurable Intelligent Surface Design for Two-Way Relay Networks
Wang, J., Liang, Y., Joung, J., Yuan, X., & Wang, X. (2021) · IEEE Transactions on Communications · doi:10.1109/tcomm.2021.3078524 · Cited by 60
Self-citationUser Scheduling and Trajectory Optimization for Energy-Efficient IRS-UAV Networks With SWIPT
Zargari, S., Hakimi, A., Tellambura, C., & Herath, S. P. (2022) · IEEE Transactions on Vehicular Technology · doi:10.1109/tvt.2022.3207700 · Cited by 60
Free Space Optical Communication: An Enabling Backhaul Technology for 6G Non-Terrestrial Networks
Elamassie, M., & Uysal, M. (2023) · Photonics · doi:10.3390/photonics10111210 · Cited by 60
Modeling RIS Empowered Outdoor-to-Indoor Communication in mmWave Cellular Networks
Nemati, M., Maham, B., Pokhrel, S. R., & Choi, J. (2021) · IEEE Transactions on Communications · doi:10.1109/tcomm.2021.3104878 · Cited by 60
Deep Reinforcement Learning for Robust Beamforming in IRS-assisted Wireless Communications
Lin, J., Zout, Y., Dong, X., Gong, S., Hoang, D. T., & Niyato, D. T. (2020) · Globecom · doi:10.1109/globecom42002.2020.9322372 · Cited by 60
Self-citationPérez-Adán, D., Fresnedo, Ó., González-Coma, J. P., & Castedo, L. (2021) · Electronics · doi:10.3390/electronics10192345 · Cited by 59
Review for wireless communication technology based on digital encoding metasurfaces
Zhan, H., Gu, M., Tian, Y., Feng, H., Zhu, M., Zhou, H.-M., Jin, Y., Tang, Y., Li, C., Fang, B., et al. (2025) · Opto-Electronic Advances · doi:10.29026/oea.2025.240315 · Cited by 59
Cao, Y., Lv, T., Lin, Z., & Ni, W. (2021) · IEEE Transactions on Cognitive Communications and Networking · doi:10.1109/tccn.2021.3064973 · Cited by 58
Link Budget Analysis for Backscatter-Based Passive IoT
Galappaththige, D., Rezaei, F., Tellambura, C., & Herath, S. P. (2022) · IEEE Access · doi:10.1109/access.2022.3227499 · Cited by 58
Xu, C., An, J., Bai, T., Xiang, L., Sugiura, S., Maunder, R. G., Yang, L., & Hanzo, L. (2022) · IEEE Transactions on Vehicular Technology · doi:10.1109/tvt.2022.3147859 · Cited by 57
Intelligent Reflecting Surfaces for Enhanced NOMA-based Visible Light Communications
Abumarshoud, H., Selim, B., Tatipamula, M., & Haas, H. (2022) · International Conference on Communications · doi:10.1109/icc45855.2022.9838853 · Cited by 56
Reinforcement Learning in the Sky: A Survey on Enabling Intelligence in NTN-Based Communications
Naous, T., Itani, M., Awad, M., & Sharafeddine, S. (2023) · IEEE Access · doi:10.1109/access.2023.3236801 · Cited by 63
Energy-efficient trajectory design for secure SWIPT systems assisted by UAV-IRS
Li, Z., Qu, S., Xu, H., Wei, Z., & Zhang, C. (2024) · Vehicular Communications · doi:10.1016/j.vehcom.2023.100725 · Cited by 56
Intelligent Reflecting Surfaces With Spatial Modulation: An Electromagnetic Perspective
Yurduseven, O., Assimonis, S. D., & Matthaiou, M. (2020) · IEEE Open Journal of the Communications Society · doi:10.1109/ojcoms.2020.3017237 · Cited by 56
Mao, Z., Peng, M., & Liu, X. (2021) · China Communications · doi:10.23919/jcc.2021.03.003 · Cited by 55
Reconfigurable Intelligent Surface Enhanced Secure Aerial-Ground Communication
Li, J., Xu, S., Liu, J., Cao, Y., & Gao, W. (2021) · IEEE Transactions on Communications · doi:10.1109/tcomm.2021.3086517 · Cited by 55
Wireless Information and Energy Transfer in the Era of 6G Communications
Psomas, C., Ntougias, K., Shanin, N., Xu, D., Mayer, K. M., Tran, N. M., Cottatellucci, L., Choi, K. W., Kim, D. I., Schober, R., et al. (2024) · Proceedings of the IEEE · doi:10.1109/jproc.2024.3395178 · Cited by 55
Self-citationA Survey of Deep Learning Architectures for Intelligent Reflecting Surfaces
Elbir, A. M., & Mishra, K. V. (2025) · IEEE Communications Standards Magazine · doi:10.1109/mcomstd.2025.3616430 · Cited by 55
Zhuang, Y., Li, X., Ji, H., & Zhang, H. (2022) · IEEE Transactions on Green Communications and Networking · doi:10.1109/tgcn.2022.3144465 · Cited by 54
Wu, J., Kim, S., & Shim, B. (2022) · IEEE Transactions on Wireless Communications · doi:10.1109/twc.2022.3182773 · Cited by 54
Feng, Q., Kong, X., Shan, M., Lin, Y., Li, L., & Cui, T. J. (2022) · Physical Review Applied · doi:10.1103/physrevapplied.17.034017 · Cited by 53
FlyReflect: Joint Flying IRS Trajectory and Phase Shift Design Using Deep Reinforcement Learning
Truong, T. P., Tuong, V. D., Dao, N., & Cho, S. (2022) · IEEE Internet of Things Journal · doi:10.1109/jiot.2022.3218740 · Cited by 53
UAVs Assisted Intelligent Reflecting Surfaces SWIPT System With Statistical CSI
Yu, K., Yu, X., & Cai, J. (2021) · IEEE Journal of Selected Topics in Signal Processing · doi:10.1109/jstsp.2021.3096025 · Cited by 52
Naeem, F., Kaddoum, G., Khan, S., Khan, K. S., & Adam, N. H. (2022) · IEEE Access · doi:10.1109/access.2022.3220682 · Cited by 52
Bansal, A., Singh, K. K., & Li, C. (2021) · IEEE Open Journal of the Communications Society · doi:10.1109/ojcoms.2021.3070340 · Cited by 52
Tran, N. M., Amri, M. M., Park, J. H., Kim, D. I., & Choi, K. W. (2022) · IEEE Internet of Things Journal · doi:10.1109/jiot.2022.3179691 · Cited by 51
Self-citationLi, R., Li, D., Ma, J., Feng, Z., Zhang, L., Tan, S., Sha, W. E. I., Chen, H., & Li, E. (2023) · IEEE Transactions on Antennas and Propagation · doi:10.1109/tap.2023.3235015 · Cited by 51
Li, Z., Chen, W., Cao, H., Tang, H., Wang, K., & Li, J. (2022) · IEEE Transactions on Vehicular Technology · doi:10.1109/tvt.2022.3196039 · Cited by 49
Wang, B., Li, H., Shen, S., Cheng, Z., & Clerckx, B. (2024) · IEEE Transactions on Communications · doi:10.1109/tcomm.2024.3447917 · Cited by 49
Decision Transformers For Wireless Communications: A New Paradigm Of Resource Management
Zhang, J., Li, J., Wang, Z., Shi, L., Jin, S., Chen, W., & Poor, H. V. (2025) · IEEE Wireless Communications · doi:10.1109/mwc.007.2400124 · Cited by 49
Energy Optimization in Dual-RIS UAV-Aided MEC-Enabled Internet of Vehicles
Michailidis, E. T., Miridakis, N. I., Michalas, A., Skondras, E., & Vergados, D. J. (2021) · Sensors · doi:10.3390/s21134392 · Cited by 49
Performance Analysis of Reconfigurable Intelligent Surface Assisted Two-Way NOMA Networks
Liu, Z., Yue, X., Zhang, C., Liu, Y., Yao, Y., Wang, Y., & Ding, Z. (2022) · IEEE Transactions on Vehicular Technology · doi:10.1109/tvt.2022.3201371 · Cited by 49
Wang, R., Xing, Z., Liu, E., & Wu, J. H. (2023) · IEEE Transactions on Communications · doi:10.1109/tcomm.2023.3249227 · Cited by 49
RIS-Aided Cell-Free Massive MIMO: Performance Analysis and Competitiveness
Al-Nahhas, B., Obeed, M., Chaaban, A., & Hossain, M. J. (2021) · IEEE International Conference on Communications workshops · doi:10.1109/iccworkshops50388.2021.9473521 · Cited by 48
Ahmed, M., Raza, S., Soofi, A. A., Khan, F., Khan, W. U., Xu, F., Chatzinotas, S., Dobre, O. A., Han, Z., & Han, Z. (2024) · Computer Science Review · doi:10.1016/j.cosrev.2024.100668 · Cited by 48
Zheng, J., Tian, H., Ni, W., Ni, W., & Zhang, P. (2022) · IEEE Transactions on Wireless Communications · doi:10.1109/twc.2022.3188502 · Cited by 47
Ramezani, P., & Jamalipour, A. (2021) · IEEE Transactions on Vehicular Technology · doi:10.1109/tvt.2021.3116708 · Cited by 47
Reconfigurable Intelligent Surface-Enabled Vehicular Networks: A Physical Layer Security Perspective
Makarfi, A. U., Rabie, K. M., Ihsan, A., Kaiwartya, O., Adhikari, K., Li, X., Quiróz-Castellanos, M., & Kharel, R. (2025) · IEEE Open Journal of the Communications Society · doi:10.1109/ojcoms.2025.3642889 · Cited by 47
Intelligent Reflecting Surface-Aided Integrated Terrestrial-Satellite Networks
Dong, H., Hua, C., Liu, L., Xu, W., & Tafazolli, R. (2022) · IEEE Transactions on Wireless Communications · doi:10.1109/twc.2022.3212049 · Cited by 45
Wang, L., Wu, W. Q., Zhou, F., Wu, Q., Dobre, O. A., & Quek, T. Q. S. (2023) · IEEE Transactions on Wireless Communications · doi:10.1109/twc.2023.3330999 · Cited by 46
Generalized Coordinated Multipoint Framework for 5G and Beyond
Solaija, M. S. J., Salman, H., Kihero, A. B., Sağlam, M. İ., & Arslan, H. (2021) · IEEE Access · doi:10.1109/access.2021.3079190 · Cited by 45
Oliveri, G., Rocca, P., Salucci, M., Erricolo, D., & Massa, A. (2022) · IEEE Transactions on Antennas and Propagation · doi:10.1109/tap.2022.3201700 · Cited by 45
Ge, J., Liang, Y., Li, S., & Bai, Z. (2022) · IEEE Transactions on Wireless Communications · doi:10.1109/twc.2022.3167474 · Cited by 44
Self-citationLarge Intelligent Surface Aided Physical Layer Security Transmission
Feng, B., Wu, Y., Zheng, M., Xia, X., Wang, Y., & Xiao, C. (2020) · IEEE Transactions on Signal Processing · doi:10.1109/tsp.2020.3021985 · Cited by 44
Reconfigurable Intelligent Surface: Reflection Design Against Passive Eavesdropping
Luo, J., Wang, F., Wang, S., Wang, H., & Wang, D. (2021) · IEEE Transactions on Wireless Communications · doi:10.1109/twc.2021.3049312 · Cited by 44
A Comprehensive Survey of 6G Wireless CommunicationsPreprint
Zhao, Y., Zhai, W., Zhao, J., Zhang, T., Sun, S., Niyato, D. T., & Lam, K. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2101.03889 · Cited by 44
Self-citationAmbient Backscatter-Assisted Wireless-Powered Relaying
Lu, X., Niyato, D. T., Jiang, H., Hossain, E., & Wang, P. (2019) · IEEE Transactions on Green Communications and Networking · doi:10.1109/tgcn.2019.2936544 · Cited by 43
Self-citationDRL based Data Offloading for Intelligent Reflecting Surface Aided Mobile Edge Computing
Zhang, X., Shen, Y., Yang, B., Zang, W., & Wang, S. (2021) · IEEE Wireless Communications and Networking Conference · doi:10.1109/wcnc49053.2021.9417469 · Cited by 43
Liu, Z., Zhao, S., Wu, Q., Yang, Y., & Guan, X. (2021) · IEEE Communications Letters · doi:10.1109/lcomm.2021.3128545 · Cited by 43
Anchor-Assisted Intelligent Reflecting Surface Channel Estimation for Multiuser Communications
Guan, X., Wu, Q., & Zhang, R. (2020) · Globecom · doi:10.1109/globecom42002.2020.9347985 · Cited by 42
Mahbub, M., & Shubair, R. M. (2023) · Journal of Network and Computer Applications · doi:10.1016/j.jnca.2023.103726 · Cited by 42
An optimized scheme for energy efficient wireless communication via intelligent reflecting surfaces
Taneja, A., Rani, S., Alhudhaif, A., Koundal, D., & Gündüz, E. S. (2021) · Expert Systems with Applications · doi:10.1016/j.eswa.2021.116106 · Cited by 42
A Survey on Intelligent-Reflecting-Surface-Assisted UAV Communications
Park, K.-W., Kim, H. M., & Shin, O. (2022) · Energies · doi:10.3390/en15145143 · Cited by 42
A varactor-based 1024-element RIS design for mm-waves
da Silva, L. G., Chu, Z., Xiao, P., & Arismar Cerqueira, S. (2023) · Frontiers in Communications and Networks · doi:10.3389/frcmn.2023.1086011 · Cited by 41
Gong, S., Cui, L., Gu, B., Lyu, B., Hoang, D. T., & Niyato, D. T. (2023) · IEEE Transactions on Wireless Communications · doi:10.1109/twc.2023.3259721 · Cited by 41
Self-citationChannel Tracking and Prediction for IRS-Aided Wireless Communications
Wei, Y. M., Zhao, M.-M., Liu, A., & Zhao, M. (2022) · IEEE Transactions on Wireless Communications · doi:10.1109/twc.2022.3196313 · Cited by 41
Asif, M., Ihsan, A., Khan, W. U., Ali, Z., Zhang, S., & Wu, S. X. (2023) · IEEE Transactions on Green Communications and Networking · doi:10.1109/tgcn.2023.3281414 · Cited by 40
Metadata
- Authors
- Gong, S., Lu, X., Hoang, D. T., Niyato, D., Shu, L., Kim, D. I., & Liang, Y.-C.
- Affiliations
- Sun Yat-sen University (CN)
- University of Alberta (CA)
- University of Technology Sydney (AU)
- Nanyang Technological University (SG)
- Nanjing Agricultural University (CN)
- Sungkyunkwan University (KR)
- University of Electronic Science and Technology of China (CN)
- Venue
- IEEE Communications Surveys & Tutorials
- Published
- 2020
- Volume
- 22
- Issue
- 4
- Pages
- 2283-2314
- ISSN
- 1553-877X (ISSN-L)
- 2373-745X
- OpenAlex
- W3027118737
- Cited by
- 1,035
- References
- 193
- Citations by year
- 2026: 77 · 2025: 164 · 2024: 167 · 2023: 194 · 2022: 212 · 2021: 184 · 2020: 36
- FWCI
- 53.77
- Type
- journal article
- Language
- en
- Open access
- green
- arxiv.org
- Licence
- Locations
- IEEE Communications Surveys & Tutorials (publishedVersion)
- arXiv (Cornell University) (submittedVersion)
- UTS ePRESS (University of Technology Sydney) (submittedVersion)
- arXiv (Cornell University)
- Funders
- National Natural Science Foundation of China (61972434, U1801261)
- Shenzhen Basic Research Program (JCYJ20190807154009444)
- Shenzhen Talent Peacock Plan Program (KQTD2015071715073798)
- PCL Future Greater-Bay Area Network Facilities for Largescale Experiments and Applications (LZC0019)
- National Research Foundation (NRF), Singapore, through Singapore Energy Market Authority (EMA), Energy Resilience, Singapore (NRF2017EWT-EP003-041, NRF2015-NRF-ISF001-2277)
- Singapore NRF National Satellite of Excellence, Design Science and Technology for Secure Critical Infrastructure (NSoE DeSTSCI2019-0007)
- A*STAR-NTU-SUTD Joint Research Grant on Artificial Intelligence for the Future of Manufacturing (RGANS1906)
- Wallenberg AI, Autonomous Systems and Software Program and Nanyang Technological University (M4082187 (4080))
- National Research Foundation of Korea (NRF) Grant
- Korean Government (2014R1A5A1011478)
- Key Areas of Research and Development Program of Guangdong Province, China (2018B010114001)
- Central Universities Funding (ZYGX2019Z022)
- Higher Education Discipline Innovation Project (B20064)
- Topics
- Physical Sciences › Engineering › Electrical and Electronic Engineering › Advanced Wireless Communication Technologies
- Physical Sciences › Engineering › Electrical and Electronic Engineering › Advanced Antenna and Metasurface Technologies
- Physical Sciences › Engineering › Electrical and Electronic Engineering › Millimeter-Wave Propagation and Modeling
- Keywords
- reconfigurable intelligent surface, smart radio environments, phase shift control, rate maximization, power minimization, beyond 5G, wireless network design
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
