Paper "Machine Learning-Based Beamforming for Unmanned Aerial Vehicles Equipped with Reconfigurable Intelligent Sur… https://t.co/eMuShuxVsG
Paper "Machine Learning-Based Beamforming for Unmanned Aerial Vehicles Equipped with Reconfigurable Intelligent Sur… https://t.co/eMuShuxVsG
Paper "Device-to-Device Relaying: Optimization, Performance Perspectives, and Open Challenges" accepted for publica… https://t.co/wQf4DedktX
Paper "Optimization of Total Power Consumed by Flying Base Station Serving Mobile Users" accepted for publication i… https://t.co/xDNkb53km9
Paper "Reducing Storage and Communication Latencies in Vehicular Edge Cloud" accepted for presentation at @EuCNC &… https://t.co/JChgzoVbyq
The aim of 6Gm is to propose new approaches and provide innovative solutions towards future 5G+ and 6G mobile networks. The 6Gm is oriented on topics related to mobility and radio resource management. This includes design of conventional as well as machine learning and artificial intelligence-based control and management algorithms for handover, power control, interference management, load balancing, self-optimization of radio access, cognitive radio, and energy efficient communications. The 6Gm also addresses network architecture issues including MEC, C-RAN and flexible radio access networks. From perspective of 5G+, 6G scenarios, our research is oriented towards scenarios encompassing device-to-device communications, flying base stations (drones, UAVs), IoT, vehicular communications, and intelligent transportation systems.