In future heterogeneous cellular networks with small cells, such as D2D and relay, interference coordination between macro cells and small cells should be addressed through effective resource allocation and power control. The two-step Stackelberg game is a widely used and feasible model for resource allocation and power control problem formulation. Both in the follower games for small cells and in the leader games for the macro cell, the cost parameters are a critical variable for the performance of Stackelberg game. Previous studies have failed to adequately address the optimization of cost parameters. This paper presents a reinforcement learning approach for effectively training cost parameters for better system performance. Furthermore, a two-stage pretraining plus
ε
-greedy algorithm is proposed to accelerate the convergence of reinforcement learning. The simulation results can demonstrate that compared with the three beachmarking algorithms, the proposed algorithm can enhance average throughput of all users and cellular users by up to 7% and 9.7%, respectively.
Deep reinforcement learning-based resource allocation for D2D communications in heterogeneous cellular networks
