This paper investigates a master unmanned aerial vehicle (MUAV)-powered Internet of Things (IoT) network, in which we propose using a rechargeable auxiliary UAV (AUAV) equipped with an intelligent reflecting surface (IRS) to enhance the communication signals from the MUAV and also leverage the MUAV as a recharging power source.
To the best of our knowledge, this is the first work to explicitly investigate joint UL-DL optimization for UAV assisted systems under heterogeneous requirements.
Low earth orbit (LEO) satellite-assisted communications have been considered as one of key elements in beyond 5G systems to provide wide coverage and cost-efficient data services.
The semantic communication system enables wireless devices to communicate effectively with the semantic meaning of the data.
First, we give an overview of 6G from perspectives of technologies, security and privacy, and applications.
However, due to the privacy concerns of devices and limited computation or communication resource of UAVs, it is impractical to send raw data of devices to UAV servers for model training.
The conventional RL/DRL, e. g., deep Q-learning, however, is limited in dealing with two main issues in constrained combinatorial optimization, i. e., exponentially increasing action space and infeasible actions.
Motivated by the observation that coordination synchronization may result in high coordination delay that can be intolerable when the network is large in scale, we propose a novel asynchronized ADMM algorithm.
Signal Processing Networking and Internet Architecture