Dynamic resource allocation plays a critical role in the next generation of intelligent wireless
communication systems. Machine learning has been leveraged as a powerful tool to make …

Recent work has demonstrated that cutting-edge advances in deep reinforcement learning
(DRL) may be leveraged to empower wireless devices with the much-needed ability to" …

Despite the success of Deep Reinforcement Learning (DRL) in radio-resource management
within multi-cell wireless networks, applying it to power allocation in ultra-dense 5G and …

Modern radios, such as 5G New Radio, feature a large set of physical-layer control knobs in
order to support an increasing number of communication scenarios spanning multiple use …

Extensive spectrum reuse due to the massive deployment of small base stations will incur
high interference in future networks. One solution to deal with this interference is …

Deep reinforcement learning (DRL) has been a successful paradigm in machine learning
that enables solving complex control problems at the human level. However, the sampling …

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 …

Multi-agent deep reinforcement learning has pre-dominantly been applied to tackle the
challenges of power allocation within complex and dynamic wireless networks. These …

Deep reinforcement learning (DRL) algorithms have recently gained wide attention in the
wireless networks domain. They are considered promising approaches for solving dynamic …

We tackle the problem of joint frequency and power allocation while emphasizing the
generalization capability of a deep reinforcement learning model. Most of the existing …