Resource Allocation of Cache-aided NOMA in 5G Vehicular Networks with Imperfect Channel Estimation

Abstract

A potential solution called cache-aided nonorthogonal multiple access (NOMA) which exploits the cached data as side information to cancel interference has been proposed to improve the quality of service (QoS) in 5G vehicular networks. In vehicular networks, providing reliable communication is challenging due to the fast variation of channel state. In this paper, the optimal power control and successive interference cancellation (SIC) ordering selection problem is investigated considering imperfect channel estimation in cache-aided NOMA vehicular networks. To guarantee fairness and improve the QoS of all vehicles, an optimization problem to maximize the minimum achieved average outrage data rate for vehicles is formulated. The formulated problem is much complex with outage probability constraints, then Markov inequality is adopted to transform the probability constraint problem into the non-probability problem. The transformed problem is still non-convex, but it can be decomposed into power control subproblem and SIC ordering selection subproblem. The suboptimal solution is obtained by solving subproblems to balance the computational complexity and system performance. Numerical results show the effectiveness of proposed scheme.