We consider a typical heterogeneous network (HetNet), in which multiple access points
(APs) are deployed to serve users by reusing the same spectrum band. Since different APs …

Deep reinforcement learning offers a model-free alternative to supervised deep learning and
classical optimization for solving the transmit power control problem in wireless networks …

Many of the existing optimization-based transmit power control algorithms suffer from high
computational complexity and require instantaneous global channel state information (CSI) …

This work demonstrates the potential of deep reinforcement learning techniques for transmit
power control in wireless networks. Existing techniques typically find near-optimal power …

Due to the scarcity in the wireless spectrum and limited energy resources especially in
mobile applications, efficient resource allocation strategies are critical in wireless networks …

Heterogeneous network (HetNet) is a promising solution to satisfy the unprecedented
demand for higher data rate in the next generation mobile networks. Different from the …

Optimizing power control in multi-cell cellular networks with deep learning enables such a
non-convex problem to be implemented in real-time. When channels are time-varying, the …

Self-organizing networks (SONs) can help to manage the severe interference in dense
heterogeneous networks (HetNets). Given their need to automatically configure power and …

This paper studies the problem of joint power allocation and user association in wireless
heterogeneous networks (HetNets) with a deep reinforcement learning (DRL)-based …

A deep neural network (DNN) based power control method that aims at solving the non-
convex optimization problem of maximizing the sum rate of a fading multi-user interference …