Terahertz-Based IRS-Assisted Secure Symbiotic Radio Communication: A DRL Approach
Developing wireless communication technologies is essential to satisfy the requirements of new applications and the increasing proliferation of interconnected devices. This research presents a resilient terahertz (THz)-based secure transmission framework for an active intelligent reflecting surface...
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| Format: | Article |
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IEEE
2025-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/10872970/ |
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| author | Muhammad Shahwar Manzoor Ahmed Touseef Hussain Sajed Ahmad Wali Ullah Khan Muhammad Sheraz Teong Chee Chuah |
| author_facet | Muhammad Shahwar Manzoor Ahmed Touseef Hussain Sajed Ahmad Wali Ullah Khan Muhammad Sheraz Teong Chee Chuah |
| author_sort | Muhammad Shahwar |
| collection | DOAJ |
| description | Developing wireless communication technologies is essential to satisfy the requirements of new applications and the increasing proliferation of interconnected devices. This research presents a resilient terahertz (THz)-based secure transmission framework for an active intelligent reflecting surface (IRS)-enabled symbiotic radio (SR) system in the presence of multiple eavesdroppers (Eves). The IRS facilitates secure transmission for the primary transmitter (PT) by intelligently adjusting the phase shifts of the signals from the PT, while simultaneously transmitting its own data to an Internet of Things (IoT) device. In light of the existence of numerous eves and unpredictable channels in real-world situations, we concurrently optimize the active beamforming of the PT and the phase shifts of the IRS to enhance the secrecy of IRS-assisted secure relay networks while adhering to quality-of-service standards and secure communication rates. To address this intricate non-convex stochastic optimization issue, we propose a secure beamforming technique named DDPG-SR, utilizing an effective deep reinforcement learning (DRL)-based deep deterministic policy gradient (DDPG) scheme to determine the optimal beamforming approach against Eves. This method seeks to establish an optimal beamforming strategy to counteract Eves under dynamic environmental circumstances. Comprehensive simulation experiments confirm the effectiveness of our proposed solution, showcasing enhanced performance relative to conventional IRS methods, IRS backscattering-based anti-evesdropping techniques, and other benchmark tactics for secrecy performance. |
| format | Article |
| id | doaj-art-e350c68d5ca649ebaa12423285cf3eae |
| institution | DOAJ |
| issn | 2169-3536 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj-art-e350c68d5ca649ebaa12423285cf3eae2025-08-20T03:01:31ZengIEEEIEEE Access2169-35362025-01-0113240142402710.1109/ACCESS.2025.353908810872970Terahertz-Based IRS-Assisted Secure Symbiotic Radio Communication: A DRL ApproachMuhammad Shahwar0https://orcid.org/0000-0001-6852-0545Manzoor Ahmed1https://orcid.org/0000-0002-0459-9845Touseef Hussain2https://orcid.org/0009-0001-6790-9003Sajed Ahmad3Wali Ullah Khan4https://orcid.org/0000-0003-1485-5141Muhammad Sheraz5https://orcid.org/0000-0001-8515-2043Teong Chee Chuah6https://orcid.org/0000-0002-6285-9481College of Computer Science and Technology, Qingdao University, Qingdao, ChinaSchool of Computer and Information Science, Hubei Engineering University, Xiaogan, ChinaSchool of Electronic Engineering, Beijing University of Posts and Telecommunications, Beijing, ChinaCollege of Computer Science and Technology, Qingdao University, Qingdao, ChinaInterdisciplinary Centre for Security, Reliability and Trust (SnT), University of Luxembourg, Esch-sur-Alzette, LuxembourgFaculty of Engineering, Multimedia University, Cyberjaya, Selangor, MalaysiaFaculty of Engineering, Multimedia University, Cyberjaya, Selangor, MalaysiaDeveloping wireless communication technologies is essential to satisfy the requirements of new applications and the increasing proliferation of interconnected devices. This research presents a resilient terahertz (THz)-based secure transmission framework for an active intelligent reflecting surface (IRS)-enabled symbiotic radio (SR) system in the presence of multiple eavesdroppers (Eves). The IRS facilitates secure transmission for the primary transmitter (PT) by intelligently adjusting the phase shifts of the signals from the PT, while simultaneously transmitting its own data to an Internet of Things (IoT) device. In light of the existence of numerous eves and unpredictable channels in real-world situations, we concurrently optimize the active beamforming of the PT and the phase shifts of the IRS to enhance the secrecy of IRS-assisted secure relay networks while adhering to quality-of-service standards and secure communication rates. To address this intricate non-convex stochastic optimization issue, we propose a secure beamforming technique named DDPG-SR, utilizing an effective deep reinforcement learning (DRL)-based deep deterministic policy gradient (DDPG) scheme to determine the optimal beamforming approach against Eves. This method seeks to establish an optimal beamforming strategy to counteract Eves under dynamic environmental circumstances. Comprehensive simulation experiments confirm the effectiveness of our proposed solution, showcasing enhanced performance relative to conventional IRS methods, IRS backscattering-based anti-evesdropping techniques, and other benchmark tactics for secrecy performance.https://ieeexplore.ieee.org/document/10872970/B5G6Gdeep reinforcement learningdeep deterministic policy gradientjoint-beamformingnon-orthogonal multiple access |
| spellingShingle | Muhammad Shahwar Manzoor Ahmed Touseef Hussain Sajed Ahmad Wali Ullah Khan Muhammad Sheraz Teong Chee Chuah Terahertz-Based IRS-Assisted Secure Symbiotic Radio Communication: A DRL Approach IEEE Access B5G 6G deep reinforcement learning deep deterministic policy gradient joint-beamforming non-orthogonal multiple access |
| title | Terahertz-Based IRS-Assisted Secure Symbiotic Radio Communication: A DRL Approach |
| title_full | Terahertz-Based IRS-Assisted Secure Symbiotic Radio Communication: A DRL Approach |
| title_fullStr | Terahertz-Based IRS-Assisted Secure Symbiotic Radio Communication: A DRL Approach |
| title_full_unstemmed | Terahertz-Based IRS-Assisted Secure Symbiotic Radio Communication: A DRL Approach |
| title_short | Terahertz-Based IRS-Assisted Secure Symbiotic Radio Communication: A DRL Approach |
| title_sort | terahertz based irs assisted secure symbiotic radio communication a drl approach |
| topic | B5G 6G deep reinforcement learning deep deterministic policy gradient joint-beamforming non-orthogonal multiple access |
| url | https://ieeexplore.ieee.org/document/10872970/ |
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