Joint beamforming and power allocation using deep learning for D2D communication in heterogeneous networks

Abstract

Device-to-device (D2D) communication plays a significant role in cellular networks as it can increase the capacity, spectrum efficiency and energy efficiency of the system. However, the large computational complexity of D2D resource management optimisation algorithms creates a serious gap between theoretical design and real-time processing, which leads to the limited use of D2D communication technology. In this study, a novel deep learning-based optimisation method is proposed to overcome the high computational complexity of joint beamforming design and power allocation optimisation algorithms in D2D communication. Unlike existing approaches, the authors design a convolutional neural network based end-to-end network structure to solve complex computing problems for channel state information under a limited feedback scenario. The Max-SE loss function which indicates quality-of-service (QoS) constraint and interference constraint, together with the mean squared error (MSE) function, are designed to maximise the spectral efficiency of the system while minimising the total transmit power. The simulation results show that the proposed approach can achieve performance comparable to the weighted minimum MSE scheme with low computation time.