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Found 6 papers, 0 papers with code
Computational Offloading in Semantic-Aware Cloud-Edge-End Collaborative Networks
To minimize long-term energy consumption on constraints queue stability and computational delay, a Lyapunov-guided deep reinforcement learning hybrid (DRLH) framework is proposed to solve the mixed integer non-linear programming (MINLP) problem.
Meta Federated Reinforcement Learning for Distributed Resource Allocation
In cellular networks, resource allocation is usually performed in a centralized way, which brings huge computation complexity to the base station (BS) and high transmission overhead.
Resource Optimization for Semantic-Aware Networks with Task Offloading
Edge computing, especially the edge intelligence system, enables local users to offload the computation tasks to the edge servers to reduce the computational energy consumption of user equipment and accelerate fast task execution.
Federated Learning for Distributed Energy-Efficient Resource Allocation
Analysis and numerical results show that the proposed FRL_suc framework can lower the transmission overhead and offload the computation from the central server to the local users, while outperforming the conventional multi-agent reinforcement learning algorithm in terms of EE, and is more robust to channel variations.
Reconfigurable Intelligent Surface Assisted Device-to-Device Communications
Reconfigurable intelligent surface (RIS) technology is a promising method to enhance wireless communications services and to realize the smart radio environment.
Reconfigurable Intelligent Surface Enhanced Device-to-Device Communications
Reconfigurable intelligent surface (RIS) technology is a promising method to enhance the device-to-device (D2D) communications.
