Interference Pricing Resource Allocation and User-Subchannel Matching for NOMA Hierarchy Fog Networks

Fog computing and non-orthogonal multiple access (NOMA) are considered to be two promising technologies due to excellent low latency and high spectrum utilization. In the past, these two techniques were often studied separately. This paper conducts the joint research on downlink NOMA hierarchical networks (HieNets) and fog computing about the energy efficiency (EE) resource allocation. We establish a two-stage Stackelberg game model with macro remote radio head (MRRH) as a leader and small remote radio heads (SRRHs) as followers. In this model, MRRH suppresses the interference generated by SRRHs through interference pricing to ensure its own data transmission. Due to the non-convexity and the non-deterministic polynomial-time hard of the EE function, we decomposed resource allocation into two parts. For the subchannel allocation part, a bilateral user-subchannel matching scheme based on the large equivalent channel gain priority is proposed. For the power allocation part, we use the power penalty method to further simplify the problem and introduce a cache reward mechanism. A pricing-based distributed iterative power allocation algorithm is proposed. Simulation results demonstrate that the proposed algorithms are superior to the existing NOMA algorithms, and the NOMA fog HieNets have great potential to enhance the performance of communication systems.