Advanced security measures in coupled phase-shift STAR-RIS networks: A DRL approach
The rapid development of next-generation wireless networks has intensified the need for robust security measures, particularly in environments susceptible to eavesdropping. Simultaneous transmitting and reflecting reconfigurable intelligent surfaces (STAR-RIS) have emerged as a transformative techno...
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| Format: | Article |
| Language: | English |
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Elsevier
2024-11-01
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| Series: | Journal of King Saud University: Computer and Information Sciences |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S1319157824003045 |
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| author | Abdul Wahid Syed Zain Ul Abideen Manzoor Ahmed Wali Ullah Khan Muhammad Sheraz Teong Chee Chuah Ying Loong Lee |
| author_facet | Abdul Wahid Syed Zain Ul Abideen Manzoor Ahmed Wali Ullah Khan Muhammad Sheraz Teong Chee Chuah Ying Loong Lee |
| author_sort | Abdul Wahid |
| collection | DOAJ |
| description | The rapid development of next-generation wireless networks has intensified the need for robust security measures, particularly in environments susceptible to eavesdropping. Simultaneous transmitting and reflecting reconfigurable intelligent surfaces (STAR-RIS) have emerged as a transformative technology, offering full-space coverage by manipulating electromagnetic wave propagation. However, the inherent flexibility of STAR-RIS introduces new vulnerabilities, making secure communication a significant challenge. To overcome these challenges, we propose a deep reinforcement learning (DRL) based secure and efficient beamforming optimization strategy, leveraging the deep deterministic policy gradient (DDPG) algorithm. By framing the problem as a Markov decision process (MDP), our approach enables the DDPG algorithm to learn optimal strategies for beamforming and transmission and reflection coefficients (TARCs) configurations. This method is specifically designed to optimize phase-shift coefficients within the STAR-RIS environment, effectively managing the coupled phase shifts and complex interactions that are critical for enhancing physical layer security (PLS). Through extensive simulations, we demonstrate that our DRL-based strategy not only outperforms traditional optimization techniques but also achieves real-time adaptive optimization, significantly improving both confidentiality and network efficiency. This research addresses key gaps in secure wireless communication and sets a new standard for future advancements in intelligent, adaptable network technologies. |
| format | Article |
| id | doaj-art-3587242dff3f4c97a15bdfcef414848f |
| institution | Kabale University |
| issn | 1319-1578 |
| language | English |
| publishDate | 2024-11-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Journal of King Saud University: Computer and Information Sciences |
| spelling | doaj-art-3587242dff3f4c97a15bdfcef414848f2024-11-28T04:34:18ZengElsevierJournal of King Saud University: Computer and Information Sciences1319-15782024-11-01369102215Advanced security measures in coupled phase-shift STAR-RIS networks: A DRL approachAbdul Wahid0Syed Zain Ul Abideen1Manzoor Ahmed2Wali Ullah Khan3Muhammad Sheraz4Teong Chee Chuah5Ying Loong Lee6The College of Computer Science and Technology, Qingdao University, Qingdao, 266071, ChinaThe College of Computer Science and Technology, Qingdao University, Qingdao, 266071, ChinaSchool of Computer and Information Science and also with Institute for AI Industrial Technology Research, Hubei Engineering University, Xiaogan, 432000, China; Corresponding authors.Interdisciplinary Centre for Security, Reliability and Trust (SnT), University of Luxembourg, Luxembourg City, L-1359, LuxembourgThe Centre for Wireless Technology, Faculty of Engineering, Multimedia University, Cyberjaya, Selangor, 63100, MalaysiaThe Centre for Wireless Technology, Faculty of Engineering, Multimedia University, Cyberjaya, Selangor, 63100, Malaysia; Corresponding authors.Faculty of Engineering and Science, Universiti Tunku Abdul Rahman, Kajang, 43000, MalaysiaThe rapid development of next-generation wireless networks has intensified the need for robust security measures, particularly in environments susceptible to eavesdropping. Simultaneous transmitting and reflecting reconfigurable intelligent surfaces (STAR-RIS) have emerged as a transformative technology, offering full-space coverage by manipulating electromagnetic wave propagation. However, the inherent flexibility of STAR-RIS introduces new vulnerabilities, making secure communication a significant challenge. To overcome these challenges, we propose a deep reinforcement learning (DRL) based secure and efficient beamforming optimization strategy, leveraging the deep deterministic policy gradient (DDPG) algorithm. By framing the problem as a Markov decision process (MDP), our approach enables the DDPG algorithm to learn optimal strategies for beamforming and transmission and reflection coefficients (TARCs) configurations. This method is specifically designed to optimize phase-shift coefficients within the STAR-RIS environment, effectively managing the coupled phase shifts and complex interactions that are critical for enhancing physical layer security (PLS). Through extensive simulations, we demonstrate that our DRL-based strategy not only outperforms traditional optimization techniques but also achieves real-time adaptive optimization, significantly improving both confidentiality and network efficiency. This research addresses key gaps in secure wireless communication and sets a new standard for future advancements in intelligent, adaptable network technologies.http://www.sciencedirect.com/science/article/pii/S1319157824003045Simultaneous transmitting and reflecting RIS (STAR-RIS)Physical layer security (PLS)Deep reinforcement learning (DRL) |
| spellingShingle | Abdul Wahid Syed Zain Ul Abideen Manzoor Ahmed Wali Ullah Khan Muhammad Sheraz Teong Chee Chuah Ying Loong Lee Advanced security measures in coupled phase-shift STAR-RIS networks: A DRL approach Journal of King Saud University: Computer and Information Sciences Simultaneous transmitting and reflecting RIS (STAR-RIS) Physical layer security (PLS) Deep reinforcement learning (DRL) |
| title | Advanced security measures in coupled phase-shift STAR-RIS networks: A DRL approach |
| title_full | Advanced security measures in coupled phase-shift STAR-RIS networks: A DRL approach |
| title_fullStr | Advanced security measures in coupled phase-shift STAR-RIS networks: A DRL approach |
| title_full_unstemmed | Advanced security measures in coupled phase-shift STAR-RIS networks: A DRL approach |
| title_short | Advanced security measures in coupled phase-shift STAR-RIS networks: A DRL approach |
| title_sort | advanced security measures in coupled phase shift star ris networks a drl approach |
| topic | Simultaneous transmitting and reflecting RIS (STAR-RIS) Physical layer security (PLS) Deep reinforcement learning (DRL) |
| url | http://www.sciencedirect.com/science/article/pii/S1319157824003045 |
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