The recent proliferation of the Industrial Internet of Things has revealed the potential of Low-
Power Wide-Area Networks as a complementary solution to cellular technologies. In this …

Reinforcement Learning (RL) algorithms have recently been proposed to solve dynamic
radio resource management (RRM) problems in beyond 5G networks. However, RL-based …

Deep Reinforcement Learning (DRL) algorithms have been recently proposed to solve
dynamic Radio Resource Management (RRM) problems in 5G networks. However, the slow …

Integrating the terrestrial network with non-terrestrial networks to provide radio access as
anticipated in the beyond 5G networks calls for efficient user association and resource …

For many years, researchers have explored power allocation (PA) algorithms driven by
models in wireless networks where multiple-user communications with interference are …

Maximizing the energy saving is one of the most important metrics in 5G and Beyond (B5G)
cellular mobile networks. In order to satisfy the diverse requirements of 5G/B5G in dynamic …

In the precision machining industry, machine tools are usually affected by various factors
during machining, and various machining errors generated accordingly. Where thermal error …

Transfer learning has achieved a lot of success recently in saving training samples.
However, most of the existing methods only focus on what and how to transfer, but ignore …

为了提升无蜂窝毫米波大规模MIMO (Cell-Free millimeter-Wave massive MIMO, CF mmWave
mMIMO) 系统总能量效率, 本文研究时变信道环境中接入点(Access Point, AP) 睡眠节能机制 …

In this paper, we formulate the traffic-aware mobile nodes sleeping with traffic offloading as a
Markov Decision Process (MDP) and solve it using Deep Reinforcement Learning (DRL) …