Joint Design for Simultaneously Transmitting And Reflecting (STAR) RIS Assisted NOMA Systems
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Abstract (arXiv)
Different from traditional reflection-only reconfigurable intelligent surfaces (RISs), simultaneously transmitting and reflecting RISs (STAR-RISs) represent a novel technology, which extends the half-space coverage to full-space coverage by simultaneously transmitting and reflecting incident signals. STAR-RISs provide new degrees-of-freedom (DoF) for manipulating signal propagation. Motivated by the above, a novel STAR-RIS assisted non-orthogonal multiple access (NOMA) (STAR-RIS-NOMA) system is proposed in this paper. Our objective is to maximize the achievable sum rate by jointly optimizing the decoding order, power allocation coefficients, active beamforming, and transmission and reflection beamforming. However, the formulated problem is non-convex with intricately coupled variables. To tackle this challenge, a suboptimal two-layer iterative algorithm is proposed. Specifically, in the inner-layer iteration, for a given decoding order, the power allocation coefficients, active beamforming, transmission and reflection beamforming are optimized alternatingly. For the outer-layer iteration, the decoding order of NOMA users in each cluster is updated with the solutions obtained from the inner-layer iteration. Moreover, an efficient decoding order determination scheme is proposed based on the equivalent-combined channel gains. Simulation results are provided to demonstrate that the proposed STAR-RIS-NOMA system, aided by our proposed algorithm, outperforms conventional RIS-NOMA and RIS assisted orthogonal multiple access (RIS-OMA) systems.
Key facts. Cited by 8 papers in the map (data through 14 Oct 2026). First posted on arXiv 6 Jun 2021.
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Citing papers per year: 2021: 1; 2022: 2; 2023: 1; 2024: 2; 2025: 1; 2026: 1.
8 papers.
- 2022Coverage and Capacity Optimization in STAR-RISs Assisted Networks: A Machine Learning Approachcited by 2
- 2021Energy-Efficient Design for a NOMA assisted STAR-RIS Network with Deep Reinforcement Learningcited by 1
- 2022DRL Enabled Coverage and Capacity Optimization in STAR-RIS Assisted Networkscited by 1
- 2024Design Optimization of NOMA Aided Multi-STAR-RIS for Indoor Environments: A Convex Approximation Imitated Reinforcement Learning Approachcited by 1
- 2023Joint User Pairing and Beamforming Design of Multi-STAR-RISs-Aided NOMA in the Indoor Environment via Multi-Agent Reinforcement Learningcited by 0
- 2024QoS Improvement in Multi User Cellular-Symbiotic Radio Network Assisted by Active-STAR-RIScited by 0
- 2025Sum Rate Maximization in STAR-RIS-UAV-Assisted Networks: A CA-DDPG Approach for Joint Optimizationcited by 0
- 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
- 2025Sum Rate Maximization in STAR-RIS-UAV-Assisted Networks: A CA-DDPG Approach for Joint Optimization1 Dec 2025
- 2024Design Optimization of NOMA Aided Multi-STAR-RIS for Indoor Environments: A Convex Approximation Imitated Reinforcement Learning Approach19 Jun 2024
- 2024QoS Improvement in Multi User Cellular-Symbiotic Radio Network Assisted by Active-STAR-RIS16 Jan 2024
- 2023Joint User Pairing and Beamforming Design of Multi-STAR-RISs-Aided NOMA in the Indoor Environment via Multi-Agent Reinforcement Learning15 Nov 2023
- 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
- 2021Energy-Efficient Design for a NOMA assisted STAR-RIS Network with Deep Reinforcement Learning30 Nov 2021