A cross-layer secure sum-rate maximization framework that jointly addresses physical-layer secrecy and network-layer packet loss in IRS–NOMA O-RAN environments and develops SecureO-RAN-SAC, a deep reinforcement learning algorithm based on Soft Actor-Critic v2, which learns near-optimal policies in real time.
Numerical results demonstrate that the proposed dynamic-role architecture consistently outperforms both a static dedicated-jammer scheme and a fully optimized all-ISAC embedded-AN benchmark, confirming that its secrecy advantage arises from adaptive spatial-functional specialization rather than from artificial-noise transmission alone.
M. M. Selim, Mohamed Rihan, Armin Dekorsy et al.· IEEE Open Journal of the Com...· 0 citations
Results indicate that the proposed AO-SCA framework provides an effective and practical solution for fairness-aware power allocation in downlink MN-NOMA systems, and provides a balanced fairness-efficiency tradeoff.
S. Dhotre, S. Nalbalwar, A. Nandgaonkar· International Research Journ...· 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
Reconfigurable intelligent surfaces (RISs) hold great potential to enhance coverage, spectral efficiency, and communication security by intelligently configuring their reflecting elements. When owned by a neutral RIS operator, these elements can be offered as resources for which legitimate receivers and eavesdroppers compete. This paper investigates such competition and evaluates its impact on the physical-layer security performance of legitimate receivers. To model the competition, we develop a sequential RIS auction (SRA) framework, in which a bundle of RIS elements is auctioned in each round through a first-price sealed-bid mechanism, with each bidder submitting its bid based on the achievable rate gain and remaining budget. We then formulate the sequential bidding process as a Markov game by specifying its states, actions, rewards, and state transitions. To solve the game, we propose a multi-bidder deep deterministic policy gradient (MADDPG)-based multi-bidder reinforcement learning (MARL) approach under centralized training and decentralized execution (CTDE), enabling legitimate receivers and eavesdroppers to learn bidding strategies that maximize their long-term economic surplus. Numerical results show that, under the considered eavesdropper bidding strategies, the RL-based strategy enables legitimate receivers to achieve the highest secrecy rate per unit cost, outperforming random and fixed strategies and approaching the ideal physical-layer upper bound.
Yuanyu Zhang, Yu Zhang, Jialu He et al.· 0 citations
This paper investigates a downlink integrated sensing and communication (ISAC) system utilizing non-orthogonal multiple access (NOMA), empowered by an active simultaneously transmitting and reflecting reconfigurable intelligent surface (STAR-RIS) operating in energy splitting mode. We maximize the communication sum rate subject to per-user quality-of-service constraints, target sensing requirements, and practical active STAR-RIS hardware constraints. The proposed formulation is further generalized to a unified framework covering active/passive STAR-RIS architectures and NOMA/space-division multiple access schemes. To solve the resulting highly non-convex problem, we develop a computationally efficient optimization framework that alternately optimizes the base station transmit beamforming and STAR-RIS beamforming by introducing a common set of auxiliary variables, thereby accelerating convergence in solving the subproblems. We also develop a worst-case robust design under norm-bounded channel state information (CSI) uncertainty, where the uncertain rate expressions are replaced with tractable conservative bounds. Simulation results show that the proposed algorithm converges much faster than fractional programming-based benchmarks, and that the active STAR-RIS assisted NOMA achieves the best performance under various system constraints. The results also demonstrate the resilience of the proposed robust design against CSI errors, while revealing that excessive active amplification may degrade the achievable sum rate under practical nonlinear amplifier distortion.
Noureen Khan, Muhammad Rehman, Jinho Choi et al.· IEEE Transactions on Wireles...· 0 citations
In single-input single-output downlink systems, the common and private streams in rate-splitting multiple access (RSMA) fully overlap in the power domain, causing strong inter-private interference. Under imperfect successive interference cancellation (SIC), this interference significantly increases users’ decoding error probability. To overcome this limitation, we propose a semi-rate-splitting multiple access (SRMA) scheme that distributes private streams across two distinct bandwidth/time regions rather than fully overlapping them. To evaluate the proposed SRMA, we formulate two design problems: 1) maximizing the minimum ergodic capacity (EC) among users and 2) minimizing the system connection outage probability (COP). Both problems jointly optimize the rate-split ratio, power allocation coefficient, and partitioned-resource factor. To address the non-convexity in these problems, we employ successive convex approximation and analytical reformulation to relax them into tractable convex problems with closed-form solutions. Monte-Carlo simulations demonstrate that SRMA outstandingly improves COP performance while achieving EC levels comparable to RSMA and outperforming conventional non-orthogonal multiple.
Thai-Hoc Vu, Anh-Tu Le, Miroslav Voznak· IEEE Wireless Communications...· 0 citations
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