Learning to optimize for resource allocation in LTE-U networks

Abstract

This paper proposes a deep learning (DL) resource allocation framework to achieve the harmonious coexistence between the transceiver pairs (TPs) and the Wi-Fi users in LTE-U networks. The noncon- vex resource allocation is considered as a constrained learning problem and the deep neural network (DNN) is employed to approximate the optimal resource allocation decisions through unsupervised manner. A parallel DNN framework is proposed to deal with the two optimization variables in this problem, where one is the licensed power allocation unit and the other is the unlicensed time fraction occupied unit. Besides, to guarantee the feasibility of the proposed algorithm, the Lagrange dual method is used to relax the constraints into the DNN training process. Then, the dual variable and the DNN parameter are alternating update via the batch-based gradient decent method until the training process converges. Numerical results show that the proposed algorithm is feasible and has better performance than other general algorithms.