DEEPCO-RIS: Joint BD-RIS and hybrid NOMA/OMA optimization for energy-efficient vehicular networks

• Published in: Sustainable computing informatics and systems Vol. 47; p. 101145
• Main Authors: Alzaben, Nada, Nemri, Nadhem, Mansouri, Wahida, Alrusaini, Othman, Ghaleb, Mukhtar, Majdoubi, Jihen
• Format: Journal Article
• Language: English
• Published: Elsevier Inc 01.09.2025
• Subjects: Beyond-diagonal RIS (BD-RIS); Block coordinate descent; Dinkelbach method; Energy efficiency; Hybrid NOMA/OMA; Successive convex approximation (SCA); Vehicular networks; Beyond-diagonal RIS (BD-RIS); Block coordinate descent; Vehicular networks; Energy efficiency; Dinkelbach method; Successive convex approximation (SCA); Hybrid NOMA/OMA
• ISSN: 2210-5379
• DOI: 10.1016/j.suscom.2025.101145

Next-generation vehicular networks require wireless infrastructures that deliver ultra-reliable, energy-efficient, and low-latency communication under highly dynamic conditions. Traditional RIS-aided and hybrid NOMA/OMA designs face critical limitations, including rigid phase control, high successive interference cancellation (SIC) complexity, and limited adaptability to rapid vehicular mobility. To address these challenges, this paper proposes DEEPCO-RIS (Dinkelbach-Enhanced Energy-Efficient Optimization with Beyond-Diagonal RIS), a unified optimization framework that integrates BD-RIS phase configuration, hybrid NOMA/OMA access mode selection, user scheduling, and power allocation. These components are jointly optimized under realistic constraints, including SIC feasibility, power budgets, RIS energy costs, and QoS guarantees. The energy efficiency maximization problem is formulated as a mixed-integer non-convex program and solved using a modular approach combining Dinkelbach’s method, block coordinate descent, successive convex approximation, and manifold-based optimization for BD-RIS tuning. Extensive simulations demonstrate that DEEPCO-RIS achieves up to 22 Mbits/Joule energy efficiency, maintains outage probabilities below 6% even under stringent QoS targets, and exhibits strong robustness against SIC imperfections and network load variations. These results establish DEEPCO-RIS as a scalable and sustainable solution for next-generation vehicular communication networks. •Propose DEEPCO-RIS: a joint BD-RIS and hybrid NOMA/OMA optimization framework.•Formulate and solve a mixed-integer non-convex energy efficiency maximization problem.•Integrate Dinkelbach’s method, block coordinate descent, SCA, and manifold optimization.•Incorporate realistic constraints: SIC feasibility, RIS hardware overhead, and QoS targets.•Achieve up to 22 Mbits/Joule energy efficiency and low outage below 6%.