Federated Multi-Agent Deep Reinforcement Learning (FMADRL) is gathering keen research interests, as it may offer efficient solutions towards meeting the extreme requirements of future wireless communication networks. By contrast to centralized Deep Reinforcement Learning (DRL) and Multi-Agent DRL (MADRL), F-MADRL enables edge devices to cooperate without sharing their private … [Read more...] about Federated Multiagent Deep Reinforcement Learning for Intelligent IoT Wireless Communications: Overview and Challenges
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Reconfigurable Intelligent Surface-Aided Near-Field Communications for 6G: Opportunities and Challenges
Reconfigurable intelligent surface (RIS)-aided near-field communications are investigated. First, the necessity of investigating RIS-aided near-field communications and the advantages brought about by the unique spherical wave-based near-field propagation are discussed. Then, the family of patch array-based RISs and metasurface-based RISs is introduced along with respective … [Read more...] about Reconfigurable Intelligent Surface-Aided Near-Field Communications for 6G: Opportunities and Challenges
How Does a Digital Twin Network Work Well for Connected and Automated Vehicles: Joint Perception, Planning, and Control
The cutting-edge technology of connected and automated vehicles (CAVs) will advance transportation systems for the foreseeable future. CAVs are expected to maintain fully automated judgment and manipulation without human intervention and, additionally, create safer driving and smarter traffic management. Digital twins (DTs) are the quiet but powerful forces enabling these new … [Read more...] about How Does a Digital Twin Network Work Well for Connected and Automated Vehicles: Joint Perception, Planning, and Control
Federated Learning-Assisted Vehicular Edge Computing: Architecture and Research Directions
Recently, realizing machine learning (ML)-based technologies with the aid of mobile edge computing (MEC) in the vehicular network to establish an intelligent transportation system (ITS) has gained considerable interest. To fully utilize the data and onboard units of vehicles, it is possible to implement federated learning (FL), which can locally train the model and centrally … [Read more...] about Federated Learning-Assisted Vehicular Edge Computing: Architecture and Research Directions
