Deep neural networks have recently emerged as a disruptive technology to solve NP-hard
wireless resource allocation problems in a real-time manner. However, the adopted neural …

The energy-efficient power control of interfering links in a large wireless network is a
challenging task. In this paper, we propose a deep learning based power control scheme …

Machine learning (ML) has been widely used for efficient resource allocation (RA) in
wireless networks. Although superb performance is achieved on small and simple networks …

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 …

The work describes how deep learning by artificial neural networks (ANNs) enables online
power allocation for energy efficiency maximization in wireless interference networks. A …

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 …

This work develops a novel power control framework for energy-efficient power control in
wireless networks. The proposed method is a new branch-and-bound procedure based on …

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 …

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

The popularity of smart mobile devices has resulted in the surged growth of mobile data
traffic, which makes current cellular communication systems overloaded. To accommodate …