Resource Allocation in Dual-RIS Cooperative Rate-Splitting Multiple Access Networks
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摘要: 速率拆分多址接入技术(RSMA)凭借灵活的干扰管理能力，被视为提升6G网络的频谱效率的关键使能技术。然而，现有RSMA方案仍存在用户公平性不足、可扩展性差及固定信道约束等问题。为此，该文提出双智能反射面(RIS)协作的RSMA通信系统，以充分挖掘RIS的信道重构潜力。系统通过双RIS间的联合反射建立级联视距链路，从而增强公共流解码性能并抑制复杂干扰。在满足用户服务质量(QoS)约束下，该文建立了以系统总传输速率最大化为目标的资源分配方案，对基站侧波束成形(BF)、速率拆分(RS)以及双RIS相位配置进行联合优化。针对变量强耦合与非凸性，该文提出了基于半正定松弛(SDR)和逐次凸逼近(SCA)的低复杂度交替优化算法。仿真结果表明，所提算法能有效收敛至高质量次优解；与现有先进方案相比，双RIS协作的RSMA系统在总传输速率上至少提升11.9%的增益，并显著改善了用户公平性和系统鲁棒性。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. -
1 基于SDR和SCA的交替优化迭代算法
Initialize: 迭代次数$ n=1 $，最大迭代次数$ {n}_{max} $，求解精度$ \epsilon $，$ {R}^{\left(0\right)}=0 $，初始化$ \boldsymbol{\theta }_{1}^{\left(0\right)} $、$ \boldsymbol{\theta }_{2}^{\left(0\right)} $、$ {\boldsymbol{w}}^{\left(0\right)} $和$ {\boldsymbol{c}}^{\left(0\right)} $. 1: while $ {R}^{\left(n\right)}-{R}^{\left(n-1\right)} \gt \epsilon $并且$ n \lt {n}_{max} $do 2: 根据$ \boldsymbol{\theta }_{2}^{\left(n-1\right)} $，$ {\boldsymbol{w}}^{\left(n-1\right)} $和$ {\boldsymbol{c}}^{\left(n-1\right)} $，求解问题$ \mathcal{P}2.1.2 $，得到$ \boldsymbol{\varphi }_{1}^{\left(n\right)} $并重构秩一解，得到$ \boldsymbol{\theta }_{1}^{\left(n\right)} $； 3: 根据$ \boldsymbol{\theta }_{1}^{\left(n\right)} $，$ {\boldsymbol{w}}^{\left(n-1\right)} $和$ {\boldsymbol{c}}^{\left(n-1\right)} $，求解问题$ \mathcal{P}2.2 $，得到$ \boldsymbol{\varphi }_{2}^{\left(n\right)} $并重构秩一解，得到$ \boldsymbol{\theta }_{2}^{\left(n\right)} $； 4： 根据$ \boldsymbol{\theta }_{1}^{\left(n\right)} $和$ \boldsymbol{\theta }_{2}^{\left(n\right)} $，求解问题$ \mathcal{P}2.3.2 $，得到$ {\boldsymbol{W}}^{\left(n\right)} $和$ {\boldsymbol{c}}^{\left(n\right)} $； 5： 更新$ \boldsymbol{\varphi }_{1}^{\left(n-1\right)} $，$ \boldsymbol{\varphi }_{2}^{\left(n-1\right)} $和$ {\boldsymbol{W}}^{\left(n-1\right)} $； 6： 分解$ {\boldsymbol{W}}^{\left(n\right)} $得到$ {\boldsymbol{w}}^{\left(n\right)} $； 7： 根据$ \boldsymbol{\theta }_{1}^{\left(n\right)} $，$ \boldsymbol{\theta }_{2}^{\left(n\right)} $，$ {\boldsymbol{w}}^{\left(n\right)} $和$ {\boldsymbol{c}}^{\left(n\right)} $计算$ {R}^{\left(n+1\right)} $； 8： $ n=n+1 $； 9: end Output:$ R^{*}=R^{(n)} $； 
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表 1 仿真参数设置
参数 取值 BS天线数 4 系统带宽 1 MHz BS最大发射功率 30 dBm 噪声功率 –80 dBm/Hz RIS相位量化系数 4 用户最小速率阈值 0.5 Mbps
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