Deep learning (DL) driven proactive resource allocation (RA) is a promising approach for
the efficient management of network resources. However, DL models typically have a …

Dynamic Spectrum Access (DSA) improves spectrum utilization by allowing secondary users
(SUs) to opportunistically access temporary idle periods in the primary user (PU) channels …

Abstract The Beyond-Fifth-Generation (B5G) Wireless Communication Systems will require
efficient resource allocation (RA) policies to fulfill future applications' increasing data rate …

We develop a framework based on deep reinforcement learning (DRL) to solve the spectrum
allocation problem in the emerging integrated access and backhaul (IAB) architecture with …

In this paper, we address the channel access problem in a dynamic wireless environment
via meta-reinforcement learning. Spectrum is a scarce resource in wireless communications …

Improving learning efficiency is paramount for learning resource allocation with deep neural
networks (DNNs) in wireless communications over highly dynamic environments …

We develop a deep reinforcement learning-based (DRL) spectrum access scheme for
device-to-device communications in an underlay cellular network. Based on the DRL …

As the demand for mobile data traffic continues to grow, offloading data traffic to unlicensed
spectrum is a promising approach that can relieve the pressure on cellular systems …

Wireless big data is attracting extensive attention from operators, vendors and academia,
which provides new freedoms in improving the performance from various levels of wireless …

To accommodate diverse Quality-of-Service (QoS) requirements in 5th generation cellular
networks, base stations need real-time optimization of radio resources in time-varying …