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

CHEN Yuang; WU Chang; PENG Mingyu; LU Hancheng

doi:10.11999/JEIT260171

Article Contents

Article Navigation > Journal of Electronics & Information Technology > 2025 >

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

Citation:

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

Citation:

PDF( 2070 KB)

Resource Allocation Optimization 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: The Joint Fund for Key Projects of National Natural Science Foundation of China (U21A20452) , The Open Fund Project of the State Key Laboratory of Advanced Communication Networks (FFX26641X001)

Received Date: 2026-01-30
Accepted Date: 2026-05-14
Rev Recd Date: 2026-05-04

Available Online: 2026-06-02

Abstract

Abstract

Objective In Rate-Splitting Multiple Access (RSMA) systems, the achievable common-stream rate is limited by the user with the weakest channel quality. This constraint reduces scalability, robustness, and user fairness in dense 6G networks. Existing cooperative RSMA architectures partly reduce this bottleneck, but they remain constrained by fixed channel conditions and limited interference management. To address these issues, this paper proposes a dual Reconfigurable Intelligent Surface (RIS) cooperative RSMA system. Two cooperatively deployed RISs create additional controllable propagation paths through cascaded double reflection. The objective is to maximize the system sum rate by jointly optimizing Base Station (BS) Beamforming (BF), Rate Splitting (RS) strategies, and dual-RIS phase configurations, thereby improving spectral efficiency, robustness, and user fairness under users’ Quality of Service (QoS) constraints. Methods A tractable system model is developed for the dual-RIS cooperative RSMA system. The model captures cascaded multi-link channels, multi-node channel structures, and interference coupling. Based on this model, a joint optimization problem is formulated to maximize the system sum rate by optimizing BS BF, RS strategies, and the discrete phase shifts of both RISs. Because of strong variable coupling and non-convexity, a low-complexity Alternating Optimization (AO) algorithm is designed. The original problem is decomposed into three subproblems: BS-side RIS phase optimization, user-side RIS phase optimization, and BS BF optimization. Semidefinite Relaxation (SDR) and Successive Convex Approximation (SCA) are used to transform these subproblems into tractable convex forms, which are then solved iteratively with fast convergence. Results and Discussions Simulation results verify the effectiveness of the proposed dual-RIS cooperative RSMA system. The proposed AO algorithm converges within six iterations under different numbers of RIS reflecting elements, and it reaches 97.8% of the steady-state sum rate within three iterations when M = 170 (Fig. 3). Compared with SOPS and RPS, the proposed phase-configuration scheme obtains 10.6% and 31.8% sum-rate gains when M = 190, respectively (Fig. 4). The proposed RSMA scheme also outperforms NOMA and SDMA by 10.0% and 14.6%, respectively (Fig. 5). Under M = 160 and b = 4, dual-RIS cooperation provides an 11.9% sum-rate gain over the single-RIS scheme, and its performance is close to the CPS upper bound (Fig. 6). Balanced allocation of reflecting elements between the two RISs further improves the sum rate (Fig. 7). The proposed BF strategy also outperforms ZF and RBF, achieving 33.2% and 336.5% gains at a transmit power of 30 dBm, respectively (Fig. 8). Under different RIS cooperation modes, the proposed joint optimization scheme achieves the best overall performance (Fig. 9). These results show that dual-RIS cooperative RSMA improves common-stream decoding, interference suppression, robustness, and user fairness. Conclusions This paper investigates a dual-RIS cooperative RSMA communication system. The proposed architecture improves common-stream decoding while mitigating complex interference. To maximize the system sum rate, BS BF vectors, RS vectors, and the discrete phase matrices of two RISs are jointly optimized. A low-complexity AO algorithm based on SDR and SCA is developed to solve the strongly coupled non-convex problem. Simulation results show that the proposed dual-RIS cooperative RSMA scheme achieves clear sum-rate gains over advanced benchmark schemes. Compared with the single-RIS mode and SDMA, it obtains 11.9% and 14.6% rate gains, respectively, while improving system robustness and user fairness.
- RSMA,
- Cooperative communication,
- Reconfigurable Intelligent Surface (RIS)

FullText(HTML)

References(23)

References

[1]	CLERCKX B, MAO Yijie, YANG Zhaohui, et al. Multiple access techniques for intelligent and multifunctional 6G: Tutorial, survey, and outlook[J]. Proceedings of the IEEE, 2024, 112(7): 832–879. doi: 10.1109/JPROC.2024.3409428.
[2]	ZHANG Yao, ZHU Yongxu, WANG Dongming, et al. A comparison between RSMA, NOMA, and SDMA in cell-free massive MIMO systems: From a secrecy perspective[J]. IEEE Transactions on Communications, 2025, 73(12): 13772–13787. doi: 10.1109/TCOMM.2025.3610223.
[3]	CHEN Yuang, LU Hancheng, QIN Langtian, et al. When xURLLC Meets NOMA: A stochastic network calculus perspective[J]. IEEE Communications Magazine, 2024, 62(6): 90–96. doi: 10.1109/MCOM.020.2300156.
[4]	QIN Langtian, LU Hancheng, CHEN Yuang, et al. Joint transmission and resource optimization in NOMA-assisted IoVT with mobile edge computing[J]. IEEE Transactions on Vehicular Technology, 2024, 73(7): 9984–9999. doi: 10.1109/TVT.2024.3364358.
[5]	MAO Yijie, DIZDAR O, CLERCKX B, et al. Rate-splitting multiple access: Fundamentals, survey, and future research trends[J]. IEEE Communications Surveys & Tutorials, 2022, 24(4): 2073–2126. doi: 10.1109/COMST.2022.3191937.
[6]	CHEN Yuang, LU Hancheng, ZHANG Chenwu, et al. Enhancing xURLLC with RSMa-assisted massive-MIMO networks: Performance analysis and optimization[J]. IEEE Transactions on Communications, 2025, 73(10): 8886–8903. doi: 10.1109/TCOMM.2025.3581971.
[7]	CLERCKX B, MAO Yijie, JORSWIECK E A, et al. A primer on rate-splitting multiple access: Tutorial, myths, and frequently asked questions[J]. IEEE Journal on Selected Areas in Communications, 2023, 41(5): 1265–1308. doi: 10.1109/JSAC.2023.3242718.
[8]	CHEN Yuang, LU Hancheng, WU Chang, et al. Performance optimization in RSMA-assisted uplink xURLLC IIoT networks with statistical QoS provisioning[J]. IEEE Transactions on Wireless Communications, 2025, 24(12): 10100–10115. doi: 10.1109/TWC.2025.3577694.
[9]	NOSRATINIA A, HUNTER T E, and HEDAYAT A. Cooperative communication in wireless networks[J]. IEEE Communications Magazine, 2004, 42(10): 74–80. doi: 10.1109/MCOM.2004.1341264.
[10]	KHISA S, ELHATTAB M, ASSI C, et al. Coordinated full-duplex cooperative transmission with RSMA in multi-cell networks[J]. IEEE Communications Letters, 2025, 29(12): 2959–2963. doi: 10.1109/LCOMM.2025.3621135.
[11]	KHISA S, ELHATTAB M, ASSI C, et al. Energy consumption optimization in RIS-assisted cooperative RSMA cellular networks[J]. IEEE Transactions on Communications, 2023, 71(7): 4300–4312. doi: 10.1109/TCOMM.2023.3270907.
[12]	YANG Junliang, GAO Shaoshuai, LU Yan, et al. Max-min fairness of cooperative RSMA for aggregated VLC/RF systems[J]. IEEE Communications Letters, 2025, 29(3): 552–556. doi: 10.1109/LCOMM.2025.3530139.
[13]	YAN Jiaqi and JIA Hou. Performance analysis of cooperative RSMA-SWIPT communication system[C]. 2025 International Conference on Computing, Networking and Communications, Honolulu, USA, 2025: 854–858. doi: 10.1109/ICNC64010.2025.10993579.
[14]	LI Xingwang, WANG Xiaoyao, ZHANG Hui, et al. QoS-aware performance analysis of full-duplex RSMA vehicle road cooperation systems[J]. IEEE Internet of Things Journal, 2024, 11(22): 36053–36065. doi: 10.1109/JIOT.2024.3424559.
[15]	SANG Xichao, GUI Lin, YING Kai, et al. Cooperative integrated communication and positioning design via rate-splitting multiple access and channel estimation enhancement[J]. IEEE Transactions on Wireless Communications, 2025, 24(3): 1786–1800. doi: 10.1109/TWC.2024.3505875.
[16]	LIU Yuanwei, LIU Xiao, MU Xidong, et al. Reconfigurable intelligent surfaces: Principles and opportunities[J]. IEEE Communications Surveys & Tutorials, 2021, 23(3): 1546–1577. doi: 10.1109/COMST.2021.3077737.
[17]	ELMOSSALLAMY M A, ZHANG Hongliang, SONG Lingyang, et al. Reconfigurable intelligent surfaces for wireless communications: Principles, challenges, and opportunities[J]. IEEE Transactions on Cognitive Communications and Networking, 2020, 6(3): 990–1002. doi: 10.1109/TCCN.2020.2992604.
[18]	WANG Wenhao, YANG Lei, ZHAN Yueying, et al. Robust resource allocation design for energy-efficient active IRS-aided C-RSMA systems[J]. IEEE Transactions on Communications, 2025, 73(7): 5168–5183. doi: 10.1109/TCOMM.2024.3506933.
[19]	WANG Dawei, LI Jiawei, LV Qinyi, et al. Integrating reconfigurable intelligent surface and AAV for enhanced secure transmissions in IoT-enabled RSMA networks[J]. IEEE Internet of Things Journal, 2025, 12(8): 9405–9419. doi: 10.1109/JIOT.2024.3523500.
[20]	PALA S, KATWE M, SINGH K, et al. Robust transmission design for RIS-aided full-duplex-RSMA V2X communications via multi-agent DRL[J]. IEEE Transactions on Vehicular Technology, 2025, 74(1): 761–775. doi: 10.1109/TVT.2024.3453253.
[21]	ZHANG Yiran, YANG Liang, LI Xingwang, et al. Covert communications for STAR-RIS-assisted industrial networks with a full duplex receiver and RSMA[J]. IEEE Internet of Things Journal, 2024, 11(12): 22483–22493. doi: 10.1109/JIOT.2024.3381166.
[22]	YANG Gang, XU Xinyue, LIANG Yingchang, et al. Reconfigurable intelligent surface-assisted non-orthogonal multiple access[J]. IEEE Transactions on Wireless Communications, 2021, 20(5): 3137–3151. doi: 10.1109/TWC.2020.3047632.
[23]	ACOSTA M R C, MORETA C E G, and KOO I. Joint power allocation and power splitting for MISO-RSMA cognitive radio systems with SWIPT and information decoder users[J]. IEEE Systems Journal, 2021, 15(4): 5289–5300. doi: 10.1109/JSYST.2020.3032725.