过去一年中添加的文章，按日期排序
Task Offloading and Resource Allocation for Wireless Powered Multi-AP Mobile Edge Computing
G SHEN, K CHI, O ALFARRAJ… - … , Communications and …, 2024 - jstage.jst.go.jp
1 天前 - … rate (SCR) in a wireless powered network having multiple APs. The devices work in
… ear programming problems, a deep reinforcement learning based algorithm is employed to …
… ear programming problems, a deep reinforcement learning based algorithm is employed to …
[HTML][HTML] Policy Compression for Intelligent Continuous Control on Low-Power Edge Devices
4 天前 - … deep reinforcement learning (DRL) models on low-power edge devices, such as
Autonomous Mobile … reliability issues incurred from wireless communication and the privacy …
Autonomous Mobile … reliability issues incurred from wireless communication and the privacy …
Deep Reinforcement Learning Based Intelligent Reflecting Surface Assisted Secure Hybrid Visible Light and Millimeter Wave Communications
DAA Saifaldeen - 2024 - search.proquest.com
4 天前 - … , contribute to the development of secure, efficient, and resilient future wireless
communication networks that are capable of meeting the demands of modern connectivity. …
communication networks that are capable of meeting the demands of modern connectivity. …
Leveraging Deep Learning on Multimodal Sensor Data for Wireless Communication: From mmWave Beamforming to Digital Twins
B Salehihikouei - 2024 - search.proquest.com
4 天前 - … reinforcement … wireless systems parameters. This dissertation explores different
scenarios, where multimodal sensor data is used to configure wireless communication networks…
scenarios, where multimodal sensor data is used to configure wireless communication networks…
Multi-Agent Deep Reinforcement Learning for Energy Efficient Multi-Hop STAR-RIS-Assisted Transmissions
PH Liao, LH Shen, PC Wu, KT Feng - arXiv preprint arXiv:2407.18627, 2024 - arxiv.org
5 天前 - … a promising way to expand coverage in wireless communications. However, limitation
of … To tackle the complex problem, a Multi-Agent Global and locAl deep Reinforcement …
of … To tackle the complex problem, a Multi-Agent Global and locAl deep Reinforcement …
Optimization of resource allocation strategy for high-speed railway based on deep reinforcement learning
X Gao, J Zhao, Q Zhang, H Han - Physical Communication, 2024 - Elsevier
5 天前 - … task offloading problem in the terrestrial communication network of high speed
trains and … Network model In this paper, we consider a HSR wireless communication system …
trains and … Network model In this paper, we consider a HSR wireless communication system …
[PDF][PDF] Training for optimal resource allocation in wireless communication systems
S Cusi - 2024 - dspace.spbu.ru
5 天前 - … Note that deep learning and reinforcement learning can … mappings, resulting in
Deep Reinforcement Learning (DRL… A cellular network is a mobile communications network that …
Deep Reinforcement Learning (DRL… A cellular network is a mobile communications network that …
Hybridized MA-DRL for Serving xURLLC with Cognizable RIS and UAV Integration
6 天前 - … multi-agent-based deep reinforcement learning (DRL) algorithm … communication
(xURLLC) is a service category in the sixth generation (6G) wireless communication networks …
(xURLLC) is a service category in the sixth generation (6G) wireless communication networks …
Applications of Multi-Agent Deep Reinforcement Learning Communication in Network Management: A Survey
Y Pi, W Zhang, Y Zhang, H Huang, B Rao… - arXiv preprint arXiv …, 2024 - arxiv.org
6 天前 - … Additionally, traffic engineering in wireless communication would consider the link
stability or data … in the V2V communication network in 5G”. In: IEEE Transactions on Cognitive …
stability or data … in the V2V communication network in 5G”. In: IEEE Transactions on Cognitive …
Diffusion Model Based Resource Allocation Strategy in Ultra-Reliable Wireless Networked Control Systems
A Babazadeh Darabi, S Coleri - arXiv e-prints, 2024 - ui.adsabs.harvard.edu
6 天前 - … Via extensive simulations, the proposed approach is shown to outperform previously
proposed Deep Reinforcement Learning (DRL) based approaches with close to optimal …
proposed Deep Reinforcement Learning (DRL) based approaches with close to optimal …