A DQN-based Joint Spectrum and Computing Resource Allocation Algorithm for MEC Networks

This paper studies the joint spectrum and computing resource allocation problem in a mobile edge computing (MEC) network, where mobile users can offload computing tasks to an edge node for processing. We formulate the joint resource allocation problem as an optimization problem with an objective to maximize the system throughput while satisfying the service delay requirements of as many service requests as possible. A novel DQN-based resource allocation algorithm is proposed to solve the formulated problem. The proposed algorithm consists of a resource pre-allocation process and a resource allocation process. The former performs joint spectrum and computing resource allocation for each service request based on a deep-Q-network (DQN) model, which attempts to make an optimal decision on resource allocation for each service request by learning online to better adapt to dynamic traffic arrivals and diverse service requirements. The latter performs resource allocation for each service request according to a resource pre-allocation decision and service requirements. Simulation results show that the proposed DQN-based resource allocation algorithm outperforms three benchmark algorithms in terms of system throughput.