Resource Optimization for NOMA-Based Secure MEC Networks with Unknown Eavesdroppers

To provide the first-class service in the Internet of Things scenario, we consider non-orthogonal multiple access (NOMA) for transmission. In this letter, we propose a joint subchannel and resource allocation framework in the uplink NOMA-based mobile edge computing network, aiming at maximizing the system’s energy efficiency (EE) in the presence of unknown eavesdroppers. Considering users’ quality of service requirement, power limitation, and latency tolerance, we decompose the formulated problem into two subproblems, namely, user-subchannel association and EE maximization. To solve these subproblems, we first employ the swapping method to find the optimal subchannel allocation scheme. Under a given user-subchannel association, we derive the closed-form expression for users’ power updating strategy during each iteration. Subsequently, a low- complexity power allocation algorithm is proposed. Numerical results demonstrate the superior performance of the proposed algorithm.