Simulation results show that the proposed Double DQN-based scheme significantly outperforms DQN and random methods in both SEE maximization and fairness enhancement, and that the NOMA system outperforms the OMA system.
Abstract
Faced with the challenge of increasing energy consumption and the need for sustainability in 6th generation (6G) wireless communication networks, this paper investigates the secrecy energy efficiency (SEE) and user fairness performance in an intelligent reflecting surface (IRS)-assisted non-orthogonal multiple access (NOMA) system in the presence of a friendly jammer and a passive eavesdropper over THz-Rician channels. We formulate both optimization objectives: maximizing the system’s total SEE and maximizing the maximum-min SEE to guarantee fairness for the worst-case user. Additionally, Jain’s fairness index is used to quantitatively evaluate the SEE balance among users. To solve these problems, we apply a Double Deep Q-Network (Double DQN)-based SEE of our proposed system model to jointly optimize power allocation and IRS phase shifts. This proposed approach enables efficient learning of optimal policies in dynamic environments without requiring explicit knowledge of the channel distribution. Furthermore, conventional Deep Q-Netowrk (DQN) and random allocation strategies are also implemented for comparison with Double DQN. Simulation results are presented for both IRS-assisted NOMA and OMA (orthogonal multiple access) systems to highlight the advantages of NOMA in terms of the secrecy-energy trade-off and spectral efficiency. Finally, the effects of system essential parameters, such as transmitted power, the number of IRS elements, atmospheric absorption coefficients, and the passive eavesdropper’s position, are examined. These simulation results show that the proposed Double DQN-based scheme significantly outperforms DQN and random methods in both SEE maximization and fairness enhancement, and that the NOMA system outperforms the OMA system. These findings confirm that the proposed model provides a basis for deploying a secure, energy-efficient, and sustainable wireless communication for future 6G networks.
The transformation to the sixth-generation (6G) vehicular-to-everything (V2X) networks requires the energy-conscious, ultra-reliable, and low-latency mechanisms of ensuring secure communication. Due to the inherent broadcast nature of vehicular channels, these systems remain vulnerable to passive eavesdropping and secrecy loss. This paper introduces a reconfigurable intelligent surface (RIS)-assisted V2X communication framework enhanced with virtual beamforming, designed to maximize secrecy rate while simultaneously improving energy efficiency. We derive closed-form expressions for secrecy outage probability (SOP) and outage probability (OP) under generalized gamma and Nakagami-
m
fading conditions and further analyze secrecy rate performance under vehicular mobility. Extensive MATLAB-based simulations validate the analytical derivations and demonstrate that the proposed RIS-assisted scheme significantly improves secrecy performance compared with conventional architectures. Results highlight the robustness of the framework across multiple mobility patterns and fading scenarios, confirming its potential as a practical building block for secure 6G-V2X systems.
R. Chawda, Premnarayan Arya, Sagar Kavaiya et al.· Journal on Wireless Communic...· 0 citations
Simulation results demonstrate that the APS NOMA scheme outperforms both the FPS NOMA and Orthogonal Multiple Access schemes, reducing OP significantly across a range of SNRs, making it highly effective for reliable and energy-efficient communication in future wireless networks.
S. Ajibowu, O. Adeleke, M. Asafa et al.· Nigerian Journal of Technolo...· 0 citations
This work investigates an IRS-assisted downlink NOMA system with untrusted users under imperfect SIC, where internal eavesdropping-users intercepting each other's messages-poses distinct security challenges absent in conventional external-eavesdropper models. A key contribution is the systematic analysis of all possible decoding orders, proving that the (2,2) order yields the largest feasible power allocation region satisfying simultaneous secrecy and QoS constraints. The joint optimization of decoding order, power allocation, and IRS reflection coefficients is decomposed into two tractable subproblems: closed-form power allocation under perfect SIC and semidefinite relaxation (SDR)-based IRS phase optimization, solved via an alternating framework. Numerical results demonstrate performance improvements of 14.6% at low power (10 W) and 12.7% at high power (80 W) over benchmark schemes, validating the effectiveness of the proposed approach in balancing secrecy and service quality under practical SIC imperfections.
Ankit Kumar, Kaneez Khatoon, Deepak Mishra et al.· International Conference on...· 0 citations
Simultaneous transmitting and reflecting reconfigurable intelligent surface (STAR-RIS) is crucial to achieve full-space coverage in next-generation wireless networks. However, optimizing resource allocation in STAR-RIS-assisted systems to balance the system sum rate with user fairness, especially in the presence of imperfect channel state information (CSI), remains a significant challenge. To address this issue, this work investigates resource allocation in an STAR-RIS-assisted multiple-input single-output system under imperfect CSI and proposes a novel method based on the deep reinforcement learning (DRL) framework to solve this problem. Specifically, the DRL framework is utilized to solve the maximization problem of the weighted sum of Jain’s fairness index and the normalized system sum rate, and a segmented training strategy is employed to decouple the complexity of the original joint optimization problem. The simulation results demonstrate that the proposed solution achieves a flexible trade-off between the system sum rate and user fairness. Moreover, it effectively mitigates the performance degradation caused by imperfect CSI, thereby ensuring robust system performance.
Lifan Zeng, Yuyang Peng, Mohammad Meraj Mirza et al.· IEEE Wireless Communications...· 0 citations
This paper investigates the rate-splitting multiple access (RSMA)-enabled integrated sensing, communication, and power transfer (ISCPT) network assisted by a simultaneously transmitting and reflecting reconfigurable intelligent surface (STAR-RIS). Particularly, considering the inherent heterogeneous quality-of-service (QoS) requirements, communication users (CUs) are granted priority in information reception, and the legitimate sensing targets (STs) are also regarded as potential eavesdroppers to intercept the information of CUs. In order to meet the service demands of heterogeneous users of such a system while ensuring the physical layer security of CUs against wiretapping, we formulate a secrecy energy efficiency (SEE) maximization problem via jointly optimizing the transmit beamforming matrix, sensing matrix, STAR-RIS reflection/transmission coefficient matrix, power splitting (PS) ratio vector, and common rate allocation vector. Due to the non-convexity of the formulated problem and the challenges caused by imperfect channel state information (CSI) and dynamic wireless environment, an agentic-AI enabled optimization approach (ISCPT-SEE-AA) is proposed, which works in a closed-loop perception-decision–reward adaptation manner. Specifically, a Transformer-based channel refinement module is developed to mitigate the uncertainty induced by imperfect CSI. Meanwhile, a mixture-of-experts group relative policy optimization (MoE-GRPO) scheme is adopted to achieve adaptive decision-making under time-varying network conditions. Furthermore, a large language model (LLM)-aided reward configuration module with retrieval-augmented generation (RAG) is integrated to automatically configure and update reward parameters, thereby reducing manual tuning overhead and enhancing training stability. Extensive simulation results verify that the proposed ISCPT-SEE-AA outperforms conventional learning-based schemes significantly in terms of SEE and exhibits stronger robustness against CSI imperfections. Notably, it achieves performance close to that of the convex optimization-based benchmark with substantially lower online computational complexity.
Wanle Zhang, Ke Xiong, Rui Dong et al.· IEEE Transactions on Cogniti...· 0 citations
Simulation results reveal that the two conflicting performance metrics of secrecy rate and total power consumption are balanced in multi‐IRS‐assisted secure communication, and highlight the superiority of the proposed method over existing benchmark schemes.
Jiaxin Li, Jianping Wang, Lei Wang et al.· Concurrency and Computation· 0 citations
