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. This paper explores a distributed resource allocation method that aims to maximize energy efficiency (EE) while ensuring the quality of service (QoS) for users. Specifically, in order to address wireless channel conditions, we propose a robust meta federated reinforcement learning (\textit{MFRL}) framework that allows local users to optimize transmit power and assign channels using locally trained neural network models, so as to offload computational burden from the cloud server to the local users, reducing transmission overhead associated with local channel state information. The BS performs the meta learning procedure to initialize a general global model, enabling rapid adaptation to different environments with improved EE performance. The federated learning technique, based on decentralized reinforcement learning, promotes collaboration and mutual benefits among users. Analysis and numerical results demonstrate that the proposed \textit{MFRL} framework accelerates the reinforcement learning process, decreases transmission overhead, and offloads computation, while outperforming the conventional decentralized reinforcement learning algorithm in terms of convergence speed and EE performance across various scenarios.Comment: Submitted to TW

Molecular imaging: Moving towards infectious diseases

AbstractMolecular imaging has been advanced into the field of infectious diseases, which provides not only new insights for basic science, but also new strategies for the effective management of infectious diseases in clinical practice

Adaptive Control of Space Robot Manipulators with Task Space Base on Neural Network

As are considered, the body posture is controlled and position cannot control, space manipulator system model is difficult to be set up because of disturbance and model uncertainty. An adaptive control strategy based on neural network is put forward. Neural network on-line modeling technology is used to approximate the system uncertain model, and the strategy avoids solving the inverse Jacobi matrix, neural network approximation error and external bounded disturbance are eliminated by variable structure control controller. Inverse dynamic model of the control strategy does not need to be estimated, also do not need to take the training process, globally asymptotically stable of the closed-loop system is proved based on the lyapunov theory. The simulation results show that the designed controller can achieve high control precision has the important value of engineering application