3 天前 - … In the realm of Internet of Things (IoT), Zigbee technology has emerged as a
cornerstone for establishing reliable, low-power, and wireless communication networks. …

4 天前 - … We first analyze the impact of wireless communication factors such as CPU frequency,
… to solve this issue, AMFL employs a deep reinforcement learning (DRL)-based method to …

5 天前 - … era, deep reinforcement learning has gradually become a research hotspot in
artificial intelligence. This study focuses on the creative use of deep reinforcement learning (DRL) …

5 天前 - … enhances the link quality in wireless communication environments. In this paper,
we propose a RIS-assisted internet of vehicles (IoV) network, considering the vehicle-to-…

6 天前 - … Deep reinforcement learning-based solutions are developed to achieve cross-domain
computation resource orchestration and deterministic transmission scheduling. The …

6 天前 - … of reinforcement learning, the Markov Decision Process. Only finite states MDP
are discussed here. This section also covers other crucial reinforcement learning concepts …

6 天前 - … the advances of wireless communications technology is … proposed application of
deep reinforcement learning (DRL)… , and network management of advanced wireless networks …

9 天前 - … reinforcement learning for slice admission control is provided to improve the slice
admission rate under limited resources and illustrate the advantages of intelligent network …

9 天前 - … In this paper, a federated multi-agent deep reinforcement learning (MADRL)
method for LISL scheduling is proposed, where the global scheduling is decomposed into …

9 天前 - … wireless network in … of reinforcement learning algorithms, we devise a joint
beamforming and bitrate control scheme, which can be wisely adapted to fluctuations in the wireless …