Integrated BD-RIS and hybrid NOMA/OMA optimization for secure, low-latency vehicular networks

To address the dual demands of low-latency and secure offloading in vehicular edge computing networks, we propose a beyond-diagonal RIS (BD-RIS)-assisted hybrid uplink framework. The BD-RIS architecture partitions elements into reflective and transmissive sub-surfaces to simultaneously support roadside units (RSUs) via non-orthogonal multiple access (NOMA) and remote processing units (RPUs) via orthogonal multiple access (OMA). The network topology comprises mobile vehicles, fixed RSUs, a centrally placed BD-RIS, and a remote RPU connected via backhaul. Tasks are dynamically classified into latency-sensitive (NOMA) and delay-tolerant (OMA) streams based on application-specific latency and data-size thresholds derived from quality of service (QoS) requirements. Vehicular mobility is modeled via time-varying positions, with simulations performed under snapshot-based quasi-static Rician fading. A cross-layer optimization problem jointly addresses BD-RIS phase configuration (under orthogonality constraints), user association, and uplink bandwidth allocation to maximize the minimum secrecy rate under mobility-driven channel variations and imperfect successive interference cancellation (SIC). We solve this mixed-integer non-convex problem via a low-complexity block coordinate descent (BCD) algorithm, which decomposes the problem into tractable subproblems using successive convex approximation (SCA). Extensive simulations of the proposed Joint BD-RIS Hybrid MEC-NOMA Offloading (JBD-RIS-HMCO) scheme demonstrate 30%–45% gains in minimum secrecy rate over traditional diagonal RIS and baseline strategies, along with fast convergence and scalability. Sensitivity studies reveal the critical roles of RIS elements, RSU density, and residual SIC interference (η). These factors significantly impact the trade-off between secure throughput and quality of service (QoS).