Conventional wireless sensors have difficulty solving the problem of energy limitation,
especially in sensor networks in hard-to-reach extreme areas. In order to solve the problem …

This paper addresses the challenge of resource allocation in wireless networks, given the
increasing usage of mobile devices and sensors. To achieve energy efficiency, we propose …

摘摇要: 深度强化学习(deep reinforcement learning, DRL) 将深度学习从高维数据提取低维
特征的能力与强化学习的决策能力相结合, 是移动通信资源管理与优化的高效算法之一 …

This paper presents a comprehensive study on the efficiency and effectiveness of
exploration policies for deep reinforcement (DRL) algorithms with applications to the power …

In this paper, a method that utilizes the reinforcement learning (RL) technique is proposed to
establish an optimal operation plan to obtain maximum power output from a trigen …

In recent years, the development of wireless communication leads to an explosive growth of
energy demand, widely application of smart devices and rapid emergence of services. So …

Energy efficiency (EE) is acknowledged as a key performance indicator for 5G networks.
This paper mainly studies the problem of energy efficient power allocation in 5G Ultra-dense …

This paper proposes a reinforcement learning-based approach that optimises bus and line
control methods to solve the problem of short circuit currents in power systems. Expansion of …

Abstract Device-to-Device (D2D) communication is one of the most promising developments
in 5G networks. D2D communication can reduce communication latency and increase …

The use of unlicensed bands on Device to Device (D2D) communication provides support
for shortage of spectrum resources. However, significant impact on the traditional unlicensed …