Coverage Characterization of STAR-RIS Networks: NOMA and OMA
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Abstract (arXiv)
The novel concept of simultaneously transmitting and reflecting reconfigurable intelligent surface (STAR-RIS) is investigated, where incident signals can be transmitted and reflected to users located at different sides of the surface. In particular, the fundamental coverage range of STAR-RIS aided two-user communication networks is studied. A sum coverage range maximization problem is formulated for both non-orthogonal multiple access (NOMA) and orthogonal multiple access (OMA), where the resource allocation at the access point and the transmission and reflection coefficients at the STAR-RIS are jointly optimized to satisfy the communication requirements of users. For NOMA, we transform the non-convex decoding order constraint into a linear constraint and the resulting problem is convex, which can be optimally solved. For OMA, we first show that the optimization problem for given time/frequency resource allocation is convex. Then, we employ the one dimensional search-based algorithm to obtain the optimal solution. Numerical results reveal that the coverage can be significantly extended by the STAR-RIS compared with conventional RISs.
Key facts. Cited by 3 papers in the map (data through 7 Oct 2026). First posted on arXiv 20 Apr 2021.
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Citing papers per year: 2022: 2; 2023: 0; 2024: 0; 2025: 0; 2026: 1.
3 papers.
- 2022Coverage and Capacity Optimization in STAR-RISs Assisted Networks: A Machine Learning Approachcited by 2
- 2022DRL Enabled Coverage and Capacity Optimization in STAR-RIS Assisted Networkscited by 1
- 2026A Particle-Swarm-Assisted Gradient Meta-Learning Algorithm for Joint Transmit Precoding and STAR-RIS Coefficient Optimizationcited by 0
- 2026A Particle-Swarm-Assisted Gradient Meta-Learning Algorithm for Joint Transmit Precoding and STAR-RIS Coefficient Optimization24 Sep 2026
- 2022DRL Enabled Coverage and Capacity Optimization in STAR-RIS Assisted Networks1 Sep 2022
- 2022Coverage and Capacity Optimization in STAR-RISs Assisted Networks: A Machine Learning Approach13 Apr 2022