In wireless network communication, in-band full-duplex technique is a useful and important technique that can enlarge the whole throughput of the wireless networks. However, its use needs harsh environment. The successive interference cancellation can make several transmitters’ data be received simultaneously by the receiver, and can make the in-band full-duplex technique be used easily in reality. In this paper, we try to propose an optimal algorithm for increasing the throughput of full-duplex multi-hop wireless networks with successive interference cancellation, which we call the full-duplex successive interference cancellation (FD-SIC) wireless networks. We first describe the mathematical model for the FD-SIC wireless networks and show it is NP-hard in general. Then, we propose a heuristic algorithm, namely the use-up-link-capacity iterative (UULC-iterative) algorithm, for each node’s routing and transmitting scheme. Simulation results show that the proposed algorithm for FD-SIC wireless networks can achieve better throughput compared with SIC-only networks and the interference avoidance networks.

The concept of vehicular edge computing (VEC) has recently been envisioned as a promising paradigm to satisfy the quality of service (QoS) requirement of delay-sensitive intelligent applications in future networks. However, the limited radio frequency communication range necessitates the dense deployment of communication infrastructures to accommodate the increasing number of connected vehicles and data traffic. This could lead to growing equipment and energy costs, hindering the full realization of the VEC system. To address such limitations, we introduce a double roadside reconfigurable intelligent surface (RIS) assisted VEC network in this paper, where RISs are deployed inside the coverage gaps between two RSUs to extend the service range. The research goal is to maximize the sum offloading efficiency by jointly optimizing offloading decisions, computation resource allocation, and phase shift vectors of RISs. Since the original problem is a challenging mixed-integer non-linear problem (MINLP), we decompose it into a top-problem for optimizing offloading destinations and phase shift vectors, and a sub-problem for optimizing offloading ratios and computation resources. We propose a deep reinforcement learning (DRL)-based algorithm for quickly obtaining near-optimal offloading decisions and phase shift vectors, alongside a Dinkelbach-based method for obtaining optimal offloading ratios and computation resource allocation. Simulation results demonstrate that our proposed algorithm achieves near-optimal performance compared to other benchmarks and enables real-time decision-making.