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.
The proposed framework does not optimize only computational speed, but also clarifies the trade-off among execution time, SINR, spectral efficiency, and fairness under dynamic uplink CF-mMIMO conditions, indicating that this architecture serves as an adaptable platform to evaluate dynamic uplink power distribution across CF-mMIMO networks.
Hussein A. Jasim, M. F. A. Rasid, F. Hashim et al.· Engineer· 0 citations
Integrated Sensing and Communication (ISAC) is emerging as a key technology for next-generation wireless networks, enabling simultaneous communication and sensing functionalities. This paper focuses a RIS-assisted full-duplex (FD) ISAC system, in which a multi-antenna base station (BS) concurrently performs multi-user uplink and downlink transmission while also carrying out radar sensing. To maximize the joint uplink–downlink sum rate, an optimization problem is formulated under practical constraints, such as radar detection SINR, self-interference, BS transmit power, user power budgets, and RIS unit-modulus conditions. To address the nonconvexity of this problem, a two-stage hybrid optimization approach is developed. In the first stage, the augmented Lagrangian technique decomposes the complex problem into simpler subproblems involving beamforming, power allocation, and RIS phase optimization, leading to a feasible initial solution. The second stage employs a Multi-Agent Deep Deterministic Policy Gradient (MADDPG) framework to refine this solution adaptively, enabling the system to respond effectively to variations in the channel environment, mobility patterns, and interference levels. The proposed hybrid framework achieves optimal resource allocation while maintaining feasibility, robustness, and adaptability. Analytical results confirm its convergence behavior, and extensive simulation results confirm that the proposed scheme consistently outperforms conventional optimization and single-agent DRL baselines in sum-rate maximization, interference mitigation, and sensing accuracy, confirming its effectiveness for RIS-assisted full-duplex ISAC systems.
S. Waqas, Fenghua Huang, Fakhar Abbas et al.· IEEE Transactions on Wireles...· 0 citations
As the role of satellites in sixth generation mobile communications system becomes increasingly well defined, satellite-terrestrial communications face an urgent need to improve spectral efficiency while ensuring user fairness. In this paper, we propose a multiple transmissive reconfigurable intelligent surfaces (RISs)-aided satellite-terrestrial downlink transmission scheme with rate-splitting multiple access. Based on statistical channel state information, we formulate a max-min user ergodic rate problem by jointly optimizing the satellite precoding, the phase shifts of multiple RISs, and the common-rate allocation. To tackle the resulting non-convex problem, we derive approximate closed-form expressions of multi-user ergodic rates and equivalently reformulate the problem via fractional programming. We then design a block coordinate descent-based algorithm to solve the transformed problem. Simulation results verify that the designed transmission scheme achieves significant performance gains in improving minimum user ergodic rate.
Kai Feng, Tianheng Xu, F. Takawira et al.· 2026 6th International Confe...· 0 citations
Near-field simultaneously transmitting and reflecting reconfigurable intelligent surface (STAR-RIS) systems enable full-space coverage, but dense multiuser operation requires active-user grouping under practical stream or RF-chain constraints. Gain-based or proportional-fair scheduling may over-serve strong users, while random selection improves service regularity but ignores channel and region information. This work proposes a randomized deficit-aware user grouping (RD-FRUG) scheme for energy-splitting STAR-RIS-aided near-field multiuser systems. RD-FRUG jointly accounts for service deficit, long-term rate imbalance, transmission/reflection region balance, and inter-user channel correlation. Given the selected group, the STAR-RIS energy-splitting coefficients, passive phase profile, and BS precoder are updated with low computational overhead. Simulation results under near-field channels with distance-dependent pathloss show that the proposed heuristic provides a favorable sum-rate–fairness tradeoff under the considered settings. It approaches the Jain’s fairness index of random selection, achieves higher sum-rate than random selection, and improves the fifth-percentile user rate over gain-greedy, proportional-fair greedy, and region-balanced baselines.
Thuan Van Le, Trong-Dai Hoang· IEEE Wireless Communications...· 0 citations
Rate-Splitting Multiple Access (RSMA) has emerged as a robust interference management strategy for future wireless networks. This paper investigates the performance of a hierarchical RSMA scheme in the downlink of a multi-antenna system, designed to efficiently serve clustered user deployments. We derive exact and asymptotic closed-form expressions for the outage probability of users under Nakagami- $m$ fading channels, considering a two-layer message splitting architecture (systemcommon, group-common, and private streams). Furthermore, to ensure fairness and reliability, we formulate a min-max power allocation problem to minimize the worst-case outage probability among users. A Geometric Programming-based algorithm is proposed to solve the resulting non-convex optimization problem. The numerical results validate the theoretical analysis and demonstrate the impact of different strategies for using this model, such as the number of users per group, user allocation strategies, and the number of base station transmit antennas.
Raphael Parreira De Souza, E. Olivo· International Mediterranean...· 0 citations
Scheduling in multiuser multiple input multiple output (MU-MIMO) systems is essential for efficient resource allocation and overall performance enhancement. In this work, a multiuser scheduling problem is formulated to maximize the product of user equipments'(UEs) aggregate satisfactions, which maintains user fairness. Solving such a combinatorial problem using exhaustive search (EX), which requires evaluating all possible multiuser groups within a massive number of resource blocks (RBs), is prohibitive. Instead, we propose an efficient users'satisfaction based scheduling approach (US-SA). In our US-SA, a low dimension sub-grouping matrix is constructed {at each frame}, which is used to schedule the best multiuser group in each time slot; satisfied users are eliminated from the scheduling process. Our US-SA performs close to the optimal EX method in terms of satisfaction, transmitted data amount, spectral efficiency, latency, and fairness with lower computational cost. Moreover, our experiments demonstrate that the proposed scheme outperforms competing techniques.