Radio Access Network (RAN) slicing is a promising technique to realize service multiplexing in the upcoming Beyond Fifth-Generation (B5G) and Sixth-Generation (6G) networks. However, limited resources and substantial interference pose an obstacle to efficient RAN slicing. In this paper, a novel Reconfigurable Intelligent Surface (RIS) assisted Cloud RAN (C-RAN) slicing framework is proposed, where enhanced Mobile BroadBand (eMBB) and Ultra-Reliable Low-Latency Communications (URLLC) services coexist via Non-Orthogonal Multiple Access (NOMA) technology. To guarantee the high system performance in considered service multiplexing scenarios, a slicing orchestration optimization problem is formulated for the joint optimization of user clustering, beamforming design, reflection coefficients design, and power allocation for each NOMA cluster. To solve the obtained non-convex problem, an Alternating Optimization (AO) based iterative algorithm is developed. Specifically, an improved K-means-based clustering algorithm is first proposed to design the NOMA clusters. Then, the problem is decoupled into three sub-problems, which are solved alternatingly by applying Fractional Programming (FP) and Successive Convex Approximation (SCA). It is proved that the proposed algorithm can converge with polynomial computational complexity. Numerical results show that the proposed algorithm can significantly improve the performance of the slicing system compared to the benchmark schemes.

Integrated sensing and communication (ISAC) is an emerging technology enhancing hardware and spectral efficiency by combining wireless communication and radar sensing functionalities. However, cooperative multi-static sensing performance in Cell-Free enabled multi-cell networks remains underexplored. To address this gap, we propose a reconfigurable intelligent surface (RIS)-assisted cooperative multi-static ISAC system comprising multiple collaborative ISAC transmitters and a centralized ISAC receiver. Specifically, the RIS introduces additional degrees of freedom (DoFs) to enhance performance by mitigating obstructions, creating virtual line-of-sight (LoS) paths, and aggregating sensing signals. To maximize sensing signal-to-noise ratio (SNR), we formulate a joint optimization problem involving radar receiver filter, active beamforming, and passive beamforming designs. Due to its non-convex nature, an alternating optimization (AO) algorithm is developed. Specifically, the radar receiver filter sub-problem is solved using a power iteration approach, while active beamforming is addressed via second-order cone programming (SOCP) and semi-definite relaxation (SDR) methods. Passive beam-forming is optimized by a successive convex approximation (SCA)-based algorithm with sequential rank-one constraint relaxation (SROCR). Simulation results confirm that: the proposed algorithm achieves significant radar SNR gain; RIS-assisted cooperative multi-static ISAC system consistently outperforms single-cell and cooperative mono-static ISAC systems; and our RIS-assisted sensing aggregation scheme significantly outperforms the scenario without RIS.