Joint Optimization of Channel Reuse and Power Allocation in Shared D2D Communication Mode

Spectral efficiency in mobile networks can be increased by allowing devices communicating directly with each other in a form of device-to-device (D2D) communication to reuse channels assigned to cellular devices, i.e., devices communicating via base station. However, the resources reuse leads to additional interference between the cellular and D2D devices. This interference can be suppressed by a smart selection of channels to be reused and allocation of transmission power to all devices at the reused channels. Since this problem is NP-hard, we propose a solution based on deep deterministic policy gradient (DDPG) for cellular channel reuse decisions combined with deep neural network (DNN) for transmission power allocation to all devices. Both machine learning models (DDPG and DNN) are naturally sub-optimal. Thus, we further extend the work towards coordinated learning of both DDPG and DNN so that a potential performance degradation due to sub-optimal outputs of DNN and DDPG is suppressed via a mutual interaction between DDPG and DNN. Simulation results show that proposed solution boosts sum capacity by up to 63 % compared to the best-performing state-of-the-art work.