Resource Allocation in Dual-RIS Cooperative Rate-Splitting Multiple Access Networks

CHEN Yuang; WU Chang; PENG Mingyu; LU Hancheng

doi:10.11999/JEIT260171

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CHEN Yuang, WU Chang, PENG Mingyu, LU Hancheng. Resource Allocation in Dual-RIS Cooperative Rate-Splitting Multiple Access Networks[J]. Journal of Electronics & Information Technology. doi: 10.11999/JEIT260171

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CHEN Yuang, WU Chang, PENG Mingyu, LU Hancheng. Resource Allocation in Dual-RIS Cooperative Rate-Splitting Multiple Access Networks[J]. Journal of Electronics & Information Technology. doi: 10.11999/JEIT260171

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Resource Allocation in Dual-RIS Cooperative Rate-Splitting Multiple Access Networks

doi: 10.11999/JEIT260171 cstr: 32379.14.JEIT260171

1.
Department of Electronic Engineering and Information Science, University of Science and Technology of China, Hefei 210016, China
2.
State Key Laboratory of Advanced Communication Networks, Shijiazhuang, 050000, China

Funds: Joint Fund for Key Projects of National Natural Science Foundation of China (U21A20452) and Open Fund Project of the State Key Laboratory of Advanced Communication Networks (FFX26641X001)

Received Date: 2026-01-30
Accepted Date: 2026-05-14

Available Online: 2026-06-02

Abstract

Abstract

Objective In RSMA systems, the achievable common-stream rate is fundamentally constrained by the user with the weakest channel quality, which limits scalability, robustness, and user fairness in dense 6G networks. Existing cooperative RSMA architectures only partially alleviate this bottleneck and still suffer from rigid channel dependencies and limited interference management capability. To address these issues, this paper proposes a dual-RIS cooperative RSMA architecture, where two collaboratively deployed RISs jointly create additional controllable propagation paths through cooperative double reflection. The objective is to maximize the system sum rate through the joint optimization of BS beamforming, RS strategies, and dual-RIS phase configurations, thereby improving spectral efficiency, robustness, and user fairness under users’ QoS constraints. Methods A tractable system model is developed for the dual-RIS cooperative RSMA architecture, accurately capturing cascaded multi-link channels and interference coupling. Based on this model, a joint optimization problem is formulated to maximize the system sum rate by optimizing BS beamforming, RS strategies, and discrete phase shifts of both RISs. Due to strong variable coupling and non-convexity, a low-complexity and efficient AO algorithm is designed, which decomposes the original problem into manageable subproblems and solves them iteratively with fast convergence. Results and Discussions Extensive simulation results demonstrate the effectiveness of the proposed dual-RIS cooperative RSMA system. The proposed AO algorithm converges rapidly within 6–7 iterations and achieves over 97% of the steady-state sum rate within three iterations for large-scale RIS deployments (Fig. 3). Compared to classic phase configuration scheme, the proposed phase configuration yields up to at least 10.6% sum-rate gains (Fig. 4). Moreover, the proposed RSMA system outperforms NOMA and SDMA by 10.0% and 14.6%, respectively (Fig. 5). Dual-RIS cooperation provides 11.9% gain over single-RIS, with performance approaching the continuous-phase upper bound (Fig. 6). Balanced RIS element allocation maximizes performance (Fig. 7). In contrast, the proposed beamforming significantly surpasses traditional methods, delivering up to at least 33.2% gains at 30 dBm transmit power (Fig. 8). These results highlight the superiority of the proposed dual-RIS cooperative RSMA system in enhancing common-stream decoding and interference suppression, leading to improved robustness and fairness. Conclusions This paper investigates a dual-RIS cooperative RSMA system that effectively improves public-stream decoding performance while mitigating complex interference. To maximize the system’s sum rate, this paper jointly optimizes BS beamforming, RS decisions, and discrete phase shifts of both RISs. A low-complexity AO algorithm is developed to address the strongly coupled non-convex problem. Extensive results demonstrate that the proposed dual-RIS cooperative RSMA scheme achieves significant sum-rate gains over state-of-the-art schemes while exhibiting superior robustness and user fairness.
- RSMA,
- Cooperative Communications,
- Reconfigurable Intelligent Surface (RIS).

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References(23)

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