Abstract
In 6 G wireless networks, secure communication is crucial due to the inherent susceptibility of electromagnetic waves to eavesdropping. Reconfigurable intelligent surfaces (RIS) and backscatter communication technologies offer promising solutions by securely directing signals to authorized users, even in the presence of multiple passive eavesdroppers equipped with multi-antenna setups. This paper proposes a novel RIS-enhanced backscatter communication system that utilizes radio frequency (RF) signals from a power beacon (PB) to transmit confidential information to multiple authorized users, each equipped with a single antenna. To optimize system performance, the deep deterministic policy gradient (DDPG) algorithm is employed to dynamically control RIS beamforming and mitigate eavesdropping attempts by adversaries using linear decoding techniques. Simulation results demonstrate that the proposed DDPG-based strategy significantly improves multicast secrecy rates while satisfying transmit power and unit modulus constraints. Compared to conventional optimization methods, the DDPG algorithm enhances the alignment of RIS reflections toward intended users and minimizes signal leakage to eavesdroppers. This research highlights how RIS and backscatter communication technologies can enhance security and energy efficiency in 6 G networks, providing a scalable solution to reduce eavesdropping threats in future wireless systems.
![](http://media.springernature.com/m312/springer-static/image/art%3A10.1007%2Fs11227-024-06819-x/MediaObjects/11227_2024_6819_Fig1_HTML.png)
![](http://media.springernature.com/m312/springer-static/image/art%3A10.1007%2Fs11227-024-06819-x/MediaObjects/11227_2024_6819_Fig2_HTML.png)
![](http://media.springernature.com/m312/springer-static/image/art%3A10.1007%2Fs11227-024-06819-x/MediaObjects/11227_2024_6819_Figa_HTML.png)
![](http://media.springernature.com/m312/springer-static/image/art%3A10.1007%2Fs11227-024-06819-x/MediaObjects/11227_2024_6819_Fig3_HTML.png)
![](http://media.springernature.com/m312/springer-static/image/art%3A10.1007%2Fs11227-024-06819-x/MediaObjects/11227_2024_6819_Fig4_HTML.png)
![](http://media.springernature.com/m312/springer-static/image/art%3A10.1007%2Fs11227-024-06819-x/MediaObjects/11227_2024_6819_Fig5_HTML.png)
![](http://media.springernature.com/m312/springer-static/image/art%3A10.1007%2Fs11227-024-06819-x/MediaObjects/11227_2024_6819_Fig6_HTML.png)
![](http://media.springernature.com/m312/springer-static/image/art%3A10.1007%2Fs11227-024-06819-x/MediaObjects/11227_2024_6819_Fig7_HTML.png)
![](http://media.springernature.com/m312/springer-static/image/art%3A10.1007%2Fs11227-024-06819-x/MediaObjects/11227_2024_6819_Fig8_HTML.png)
![](http://media.springernature.com/m312/springer-static/image/art%3A10.1007%2Fs11227-024-06819-x/MediaObjects/11227_2024_6819_Fig9_HTML.png)
![](http://media.springernature.com/m312/springer-static/image/art%3A10.1007%2Fs11227-024-06819-x/MediaObjects/11227_2024_6819_Fig10_HTML.png)
![](http://media.springernature.com/m312/springer-static/image/art%3A10.1007%2Fs11227-024-06819-x/MediaObjects/11227_2024_6819_Fig11_HTML.png)
![](http://media.springernature.com/m312/springer-static/image/art%3A10.1007%2Fs11227-024-06819-x/MediaObjects/11227_2024_6819_Fig12_HTML.png)
Similar content being viewed by others
Data Availability
No datasets were generated or analysed during the current study.
References
Shiu Y-S, Chang SY, Wu H-C, Huang SC-H, Chen H-H (2011) Physical layer security in wireless networks: a tutorial. IEEE Wirel Commun 18(2):66–74. https://doi.org/10.1109/MWC.2011.5751298
Mukherjee A, Fakoorian SAA, Huang J, Swindlehurst AL (2014) Principles of physical layer security in multiuser wireless networks: a survey. IEEE Commun Surv Tutor 16(3):1550–1573. https://doi.org/10.1109/SURV.2014.012314.00178
Feng Y, Yan S, Yang Z, Yang N, Yuan J (2018) User and relay selection with artificial noise to enhance physical layer security. IEEE Trans Veh Technol 67(11):10906–10920. https://doi.org/10.1109/TVT.2018.2870280
Li Q, Yang L (2019) Beamforming for cooperative secure transmission in cognitive two-way relay networks. IEEE Trans Inf Forensics Secur 15:130–143. https://doi.org/10.1109/TIFS.2019.2918431
Wang W, Teh KC, Li KH (2017) Artificial noise aided physical layer security in multi-antenna small-cell networks. IEEE Trans Inf Forensics Secur 12(6):1470–1482. https://doi.org/10.1109/TIFS.2017.2663336
Trappe W (2015) The challenges facing physical layer security. IEEE Commun Mag 53(6):16–20. https://doi.org/10.1109/MCOM.2015.7120011
Jin C, Hu F, Ling Z, Mao Z, Chang Z, Li C (2022) Transmission optimization and resource allocation for wireless powered dense vehicle area network with energy recycling. IEEE Trans Veh Technol 71(11):12291–12303. https://doi.org/10.1109/TVT.2022.3195216
Lipps C, Herbst J, Reddy R, Franke L, Becker S, Rahm M, Schotten HD (2022) Reconfigurable intelligent surfaces: a physical layer security perspective. In: 2022 4th International Conference on Data Intelligence and Security (ICDIS), pp. 174–181. IEEE
Zhang S, Huang W, Liu Y (2024) A systematic survey on physical layer security oriented to reconfigurable intelligent surface empowered 6g. Comput Secur. https://doi.org/10.1016/j.cose.2024.104100
Ahmed M, Wahid A, Laique SS, Khan WU, Ihsan A, Xu F, Chatzinotas S, Han Z (2023) A survey on STAR-RIS: use cases, recent advances, and future research challenges. IEEE Internet Things J. 10(16):14689–14711. https://doi.org/10.1109/JIOT.2023.3279357
Zhu Y, Mao B, Kato N (2022) A dynamic task scheduling strategy for multi-access edge computing in IRS-aided vehicular networks. IEEE Trans Emerg Top Comput 10(4):1761–1771. https://doi.org/10.1109/TETC.2022.3153494
Hashida H, Kawamoto Y, Kato N, Iwabuchi M, Murakami T (2022) Mobility-aware user association strategy for IRS-aided mm-wave multibeam transmission towards 6G. IEEE J Sel Areas Commun 40(5):1667–1678. https://doi.org/10.1109/JSAC.2022.3143216
Hashida H, Kawamoto Y, Kato N (2020) Intelligent reflecting surface placement optimization in air-ground communication networks toward 6G. IEEE Wirel Commun 27(6):146–151. https://doi.org/10.1109/MWC.001.2000142
Xu S, Liu J, Rodrigues TK, Kato N (2022) Robust multiuser beamforming for IRS-enhanced near-space downlink communications coexisting with satellite system. IEEE Internet Things J 9(16):14900–14912. https://doi.org/10.1109/JIOT.2021.3112595
Abideen SZU, Wahid A, Kamal MM (2024) Adaptive security solutions for noma networks: the role of ddpg and RIS-equipped UAVS. Int J Electr, Energy Power Syst Eng 7(3):158–174
Liu R, Zheng S, Wu Q, Jiang Y, Zhang N, Liu Y, Di Renzo M, et al (2024) Sustainable wireless networks via reconfigurable intelligent surfaces (RISS): Overview of the etsi isg ris. arXiv preprint arXiv:2406.05647
Waleed S, Ullah I, Khan WU, Rehman AU, Rahman T, Li S (2021) Resource allocation of 5g network by exploiting particle swarm optimization. Iran J Comput Sci 4(3):211–219
Han K, Huang K (2017) Wirelessly powered backscatter communication networks: modeling, coverage, and capacity. IEEE Trans Wirel Commun 16(4):2548–2561. https://doi.org/10.1109/TWC.2017.2665629
Abideen SZU, Wahid A, Kamal MM, Hussain T, Jan N (2024) Enhancing noma network security with RIS-UAV integration: exploring PPO technique. Asian Bull. Big Data Manag 4(02):4
Wahid A, Abideen SZU, Ahmed M, Khan WU, Sheraz M, Chee T, Lee YL (2024) Advanced security measures in coupled phase-shift STAR-RIS networks: a DRL approach. J King Saud Univ-Comput Inf Sci 36(9):102215
Zhou H, Hu C, Liu X (2024) An overview of machine learning-enabled optimization for reconfigurable intelligent surfaces-aided 6g networks: From reinforcement learning to large language models. arXiv preprint arXiv:2405.17439
Puspitasari AA, Lee BM (2023) A survey on reinforcement learning for reconfigurable intelligent surfaces in wireless communications. Sensors 23(5):2554
Li B, Liu W, Xie W (2024) Joint resource allocation and beamforming design for RIS-aided symbiotic radio networks: a DRL approach. Digit Commun Netw. https://doi.org/10.1016/j.dcan.2024.03.002
Tang W, Dai JY, Chen M, Li X, Cheng Q, Jin S, Wong K-K, Cui TJ (2019) Programmable metasurface-based RF chain-free 8PSK wireless transmitter. Electron Lett 55(7):417–420
Tang W, Dai JY, Chen MZ, Wong K-K, Li X, Zhao X, Jin S, Cheng Q, Cui TJ (2020) MIMO transmission through reconfigurable intelligent surface: system design, analysis, and implementation. IEEE J Sel Areas Commun 38(11):2683–2699. https://doi.org/10.1109/JSAC.2020.3007055
Jung M, Saad W, Debbah M, Hong CS (2021) On the optimality of reconfigurable intelligent surfaces (RISS): passive beamforming, modulation, and resource allocation. IEEE Trans Wireless Commun 20(7):4347–4363. https://doi.org/10.1109/TWC.2021.3058366
Khan I, Zhang K, Wu Q, Ullah I, Ali L, Ullah H, Rahman SU (2022) A wideband high-isolation microstrip MIMO circularly-polarized antenna based on parasitic elements. Materials 16(1):103
Khan HU, Sohail M, Ali F, Nazir S, Ghadi YY, Ullah I (2023) Prioritizing the multi-criterial features based on comparative approaches for enhancing security of IoT devices. Phys Commun 59:102084
Zhao W, Wang G, Atapattu S, Tsiftsis TA, Tellambura C (2020) Is backscatter link stronger than direct link in reconfigurable intelligent surface-assisted system? IEEE Commun Lett 24(6):1342–1346. https://doi.org/10.1109/LCOMM.2020.2980510
Xu S, Liu J, Cao Y (2022) Intelligent reflecting surface empowered physical-layer security: signal cancellation or jamming? IEEE Internet Things J 9(2):1265–1275. https://doi.org/10.1109/JIOT.2021.3079325
Wang C, Li Z, Zheng T-X, Ng DWK, Al-Dhahir N (2021) Intelligent reflecting surface-aided secure broadcasting in millimeter wave symbiotic radio networks. IEEE Trans Veh Technol 70(10):11050–11055. https://doi.org/10.1109/TVT.2021.3108452
Yao J, Wu T, Zhang Q, Qin J (2020) Proactive monitoring via passive reflection using intelligent reflecting surface. IEEE Commun Lett 24(9):1909–1913. https://doi.org/10.1109/LCOMM.2020.3001255
Zhao H, Shuang Y, Wei M, Cui TJ, Hougne Pd, Li L (2020) Metasurface-assisted massive backscatter wireless communication with commodity Wi-Fi signals. Nat Commun 11(1):3926
Xu S, Liu J, Zhang J (2021) Resisting undesired signal through IRS-based backscatter communication system. IEEE Commun Lett 25(8):2743–2747. https://doi.org/10.1109/LCOMM.2021.3077093
Ali BS, Ullah I, Al Shloul T, Khan IA, Khan I, Ghadi YY, Abdusalomov A, Nasimov R, Ouahada K, Hamam H (2024) ICS-IDS: application of big data analysis in Ai-based intrusion detection systems to identify cyberattacks in ICS networks. J Supercomput 80(6):7876–7905
Renzo MD, Debbah M, Phan-Huy D-T, Zappone A, Alouini M-S, Yuen C, Sciancalepore V, Alexandropoulos GC, Hoydis J, Gacanin H (2019) Smart radio environments empowered by reconfigurable AI meta-surfaces: an idea whose time has come. EURASIP J Wirel Commun Netw 2019(1):1–20
Wu Q, Zhang R (2019) Towards smart and reconfigurable environment: intelligent reflecting surface aided wireless network. IEEE Commun Mag 58(1):106–112
Zhao M-M, Wu Q, Zhao M-J, Zhang R (2020) Intelligent reflecting surface enhanced wireless networks: two-timescale beamforming optimization. IEEE Trans Wirel Commun 20(1):2–17
Goel S, Negi R (2008) Guaranteeing secrecy using artificial noise. IEEE Trans Wirel Commun 7(6):2180–2189
Jian M, Alexandropoulos GC, Basar E, Huang C, Liu R, Liu Y, Yuen C (2022) Reconfigurable intelligent surfaces for wireless communications: overview of hardware designs, channel models, and estimation techniques. Intell Converg Netw 3(1):1–32
Zhu H, Zhan J, Lam C-T, Chen B, Ng BK (2024) Machine learning based blind signal detection for ambient backscatter communication systems. IEEE Trans Cognitive Commun Netw. https://doi.org/10.1109/TCCN.2024.3457532
Yang G, Xu X, Liang Y-C, Di Renzo M (2021) Reconfigurable intelligent surface-assisted non-orthogonal multiple access. IEEE Trans Wirel Commun 20(5):3137–3151
Cheng X, Lin Y, Shi W, Li J, Pan C, Shu F, Wu Y, Wang J (2021) Joint optimization for RIS-assisted wireless communications: from physical and electromagnetic perspectives. IEEE Trans Commun 70(1):606–620
Yang H, Liu S, Xiao L, Zhang Y, Xiong Z, Zhuang W (2023) Learning-based reliable and secure transmission for UAV-RIS-assisted communication systems. IEEE Trans Wirel Commun. https://doi.org/10.1109/TWC.2023.3336535
Guo K, Wu M, Li X, Lin Z, Tsiftsis TA (2024) Joint trajectory and beamforming optimization for federated DRL-aided space-aerial-terrestrial relay networks with RIS and RSMA. IEEE Trans Wirel Commun. https://doi.org/10.1109/TWC.2024.3468298
Zhao Y, Clerckx B (2024) Riscatter: Unifying backscatter communication and reconfigurable intelligent surface. IEEE Journal on Selected Areas in Communications
ElMossallamy MA, Zhang H, Song L, Seddik KG, Han Z, Li GY (2020) Reconfigurable intelligent surfaces for wireless communications: principles, challenges, and opportunities. IEEE Trans Cognitive Commun Netw 6(3):990–1002. https://doi.org/10.1109/TCCN.2020.2992604
Özdogan Ö, Björnson E, Larsson EG (2020) Intelligent reflecting surfaces: physics, propagation, and pathloss modeling. IEEE Wirel Commun Lett 9(5):581–585. https://doi.org/10.1109/LWC.2019.2960779
Zhao W, Wang G, Atapattu S, Tsiftsis TA, Ma X (2020) Performance analysis of large intelligent surface aided backscatter communication systems. IEEE Wirel Commun Lett 9(7):962–966. https://doi.org/10.1109/LWC.2020.2976934
Abeywickrama S, You C, Zhang R, Yuen C (2021) Channel estimation for intelligent reflecting surface assisted backscatter communication. IEEE Wirel Commun Lett 10(11):2519–2523. https://doi.org/10.1109/LWC.2021.3106165
Jia X, Zhou X, Niyato D, Zhao J (2022) Intelligent reflecting surface-assisted bistatic backscatter networks: joint beamforming and reflection design. IEEE Trans Green Commun Netw 6(2):799–814. https://doi.org/10.1109/TGCN.2021.3127190
Nemati M, Ding J, Choi J (2020) Short-range ambient backscatter communication using reconfigurable intelligent surfaces. In: 2020 IEEE Wireless Communications and Networking Conference (WCNC), Seoul, Korea (South), pp. 1–6. https://doi.org/10.1109/WCNC45663.2020.9120813
Idrees S, Jia X, Durrani S, Zhou X (2022) Design of intelligent reflecting surface (IRS)-boosted ambient backscatter systems. IEEE Access 10:65000–65010. https://doi.org/10.1109/ACCESS.2022.3184017
Liang Y-C, Zhang Q, Larsson EG, Li GY (2020) Symbiotic radio: cognitive backscattering communications for future wireless networks. IEEE Trans Cognitive Commun Netw 6(4):1242–1255. https://doi.org/10.1109/TCCN.2020.3023139
Ma H, Zhang H, Zhang N, Wang J, Wang N, Leung VCM (2022) Reconfigurable intelligent surface with energy harvesting assisted cooperative ambient backscatter communications. IEEE Wirel Commun Lett 11(6):1283–1287. https://doi.org/10.1109/LWC.2022.3164257
Zhao Y, Clerckx B (2022) RIScatter: unifying backscatter communication and reconfigurable intelligent surface. arXiv preprint arXiv:2212.09121
Fara R, Phan-Huy D-T, Ratajczak P, Ourir A, Di Renzo M, De Rosny J (2021) Reconfigurable intelligent surface-assisted ambient backscatter communications - experimental assessment. In: 2021 IEEE International Conference on Communications Workshops (ICC Workshops), Montreal, QC, Canada, pp. 1–7. https://doi.org/10.1109/ICCWorkshops50388.2021.9473842
Zuo J, Liu Y, Yang L, Song L, Liang Y-C (2021) Reconfigurable intelligent surface enhanced noma assisted backscatter communication system. IEEE Trans Veh Technol 70(7):7261–7266
Zhuang Y, Li X, Ji H, Zhang H (2022) Exploiting intelligent reflecting surface for energy efficiency in ambient backscatter communication-enabled noma networks. IEEE Trans Green Commun Netw 6(1):163–174
Xu S, Liu J, Zhang J (2021) Resisting undesired signal through IRS-based backscatter communication system. IEEE Commun Lett 25(8):2743–2747
Xu S, Liu J, Cao Y (2021) Intelligent reflecting surface empowered physical-layer security: signal cancellation or jamming? IEEE Internet Things J 9(2):1265–1275
Loku Galappaththige D, Rezaei F, Tellambura C, Herath S (2023) RIS-empowered ambient backscatter communication systems. IEEE Wirel Commun Lett 12(1):173–177. https://doi.org/10.1109/LWC.2022.3220158
Ullah I, Noor A, Nazir S, Ali F, Ghadi YY, Aslam N (2024) Protecting iot devices from security attacks using effective decision-making strategy of appropriate features. J Supercomput 80(5):5870–5899
Wu N, Wang X, Fei Z, Xia F, Huang J, Nallanathan A (2024) Ris-assisted integrated sensing and backscatter communications for future iot networks. IEEE Internet Things Magazine 7(4):44–50. https://doi.org/10.1109/IOTM.001.2300184
Tu Z, Long R, Pei Y, Liang Y-C (2024) RIS-enabled full-duplex backscatter communication in multi-user symbiotic radio. IEEE Trans Wirel Commun. https://doi.org/10.1109/TWC.2024.3439622
Zou Y, Liu Y, Mu X, Zhang X, Liu Y, Yuen C (2023) Machine learning in RIS-assisted noma iot networks. IEEE Internet Things J 10(22):19427–19440. https://doi.org/10.1109/JIOT.2023.3245288
Jia X, Zhou X (2021) IRS-assisted ambient backscatter communications utilizing deep reinforcement learning. IEEE Wirel Commun Lett 10(11):2374–2378. https://doi.org/10.1109/LWC.2021.3100901
Idrees S, Jia X, Khan S, Durrani S, Zhou X (May, 2022) Deep learning based passive beamforming for IRS-assisted monostatic backscatter systems. In: ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Singapore, Singapore, pp. 8652–8656. https://doi.org/10.1109/ICASSP43922.2022.9746747
Gao J, Khandaker MRA, Tariq F, Wong K-K, Khan RT (Sept, 2019) Deep neural network based resource allocation for V2X communications. In: 2019 IEEE 90th Vehicular Technology Conference (VTC2019-Fall), Honolulu, HI, USA, pp. 1–5. https://doi.org/10.1109/VTCFall.2019.8891446
Dai L, Jiao R, Adachi F, Poor HV, Hanzo L (2020) Deep learning for wireless communications: an emerging interdisciplinary paradigm. IEEE Wirel Commun 27(4):133–139. https://doi.org/10.1109/MWC.001.1900491
François-Lavet V, Henderson P, Islam R, Bellemare MG, Pineau J (2018) An introduction to deep reinforcement learning. Found Trends ® in Mach Learn 11:219–354
Ullah I, Adhikari D, Su X, Palmieri F, Wu C, Choi C (2024) Integration of data science with the intelligent iot (iiot): current challenges and future perspectives. Digit Commun Netw. https://doi.org/10.1016/j.dcan.2024.02.007
Zhang Z, Zhang D, Qiu RC (2019) Deep reinforcement learning for power system applications: an overview. CSEE J Power Energy Syst 6(1):213–225
Kaelbling LP, Littman ML, Moore AW (1996) Reinforcement learning: a survey. J Artif Intell Res 4:237–285
Ullah I, Khan IU, Ouaissa M, Ouaissa M, El Hajjami S (2024) Future Communication Systems Using Artificial Intelligence, Internet of Things and Data Science. CRC Press, Boca Raton
Xiang J, Li Q, Dong X, Ren Z (2019) Continuous control with deep reinforcement learning for mobile robot navigation. In: 2019 Chinese Automation Congress (CAC), pp. 1501–1506. IEEE
Fujimoto S, Hoof H, Meger D (2018) Addressing function approximation error in actor-critic methods. In: International Conference on Machine Learning, pp. 1587–1596. PMLR
Yang H, Xiong Z, Zhao J, Niyato D, Xiao L, Wu Q (2020) Deep reinforcement learning-based intelligent reflecting surface for secure wireless communications. IEEE Trans Wirel Commun 20(1):375–388
Van Hasselt H, Guez A, Silver D (2016) Deep reinforcement learning with double q-learning. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 30
Haarnoja T, Zhou A, Hartikainen K, Tucker G, Ha S, Tan J, Kumar V, Zhu H, Gupta A, Abbeel P, et al (2018) Soft actor-critic algorithms and applications. arXiv preprint arXiv:1812.05905
Tang W, Chen MZ, Chen X, Dai JY, Han Y, Di Renzo M, Zeng Y, Jin S, Cheng Q, Cui TJ (2020) Wireless communications with reconfigurable intelligent surface: path loss modeling and experimental measurement. IEEE Trans Wirel Commun 20(1):421–439
Author information
Authors and Affiliations
Contributions
SZA contributed to conceptualization, methodology, software, writing—original draft, and software. AW contributed to conceptualization, methodology, and software; NS and MMK contributed to conceptualization and methodology; NI contributed to software conceptualization and methodology; NS and YID contributed to validation, resources, writing—review, and editing; MAK contributed to validation, resources; AA contributed to resources, validation, and software; IU contributed to validation, writing—review, and editing, supervision, project administration, and funding acquisition
Corresponding author
Ethics declarations
Competing interests
The authors declare no competing interests.
Additional information
Publisher's Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Rights and permissions
Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.
About this article
Cite this article
Abideen, S.Z.U., Wahid, A., Kamal, M.M. et al. Advancements in IoT system security: a reconfigurable intelligent surfaces and backscatter communication approach. J Supercomput 81, 362 (2025). https://doi.org/10.1007/s11227-024-06819-x
Accepted:
Published:
DOI: https://doi.org/10.1007/s11227-024-06819-x