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Network-Aided Intelligent Traffic Steering in 6G O-RAN: A Multi-Layer Optimization Framework
To enable an intelligent, programmable and multi-vendor radio access network (RAN) for 6G networks, considerable efforts have been made in standardization and development of open RAN (O-RAN).
Edge Computing for Semantic Communication Enabled Metaverse: An Incentive Mechanism Design
Fourth, we present the design of the DL-based auction for edge resource allocation in SemCom-enabled Metaverse.
Incentive Mechanisms for Federated Learning: From Economic and Game Theoretic Perspective
In this paper, we provide a comprehensive review for the economic and game theoretic approaches proposed in the literature to design various schemes for stimulating data owners to participate in FL training process.
Applications of Multi-Agent Reinforcement Learning in Future Internet: A Comprehensive Survey
Then, we review a number of applications of MARL to solve emerging issues in future Internet.
Dynamic Network Service Selection in Intelligent Reflecting Surface-Enabled Wireless Systems: Game Theory Approaches
To formulate the SP and network service selection, we adopt an evolutionary game in which the users are able to adapt their network selections depending on the utilities that they achieve.
Computer Science and Game Theory
Toward Smart Security Enhancement of Federated Learning Networks
In this way, the EDs in FLNs can keep training data locally, which preserves privacy and reduces communication overheads.
Radio Resource Management in Joint Radar and Communication: A Comprehensive Survey
First, we give fundamental concepts of JRC, important performance metrics used in JRC systems, and applications of the JRC systems.
Resource Management for Blockchain-enabled Federated Learning: A Deep Reinforcement Learning Approach
However, the issue of BFL is that the mobile devices have energy and CPU constraints that may reduce the system lifetime and training efficiency.
Resource Allocation in Mobility-Aware Federated Learning Networks: A Deep Reinforcement Learning Approach
However, federated learning faces the energy constraints of the workers and the high network resource cost due to the fact that a number of global model transmissions may be required to achieve the target accuracy.
Efficient Training Management for Mobile Crowd-Machine Learning: A Deep Reinforcement Learning Approach
In this letter, we propose to adopt a deep- Q learning algorithm that allows the server to learn and find optimal decisions without any a priori knowledge of network dynamics.
Networking and Internet Architecture
Applications of Deep Reinforcement Learning in Communications and Networking: A Survey
Reinforcement learning has been efficiently used to enable the network entities to obtain the optimal policy including, e. g., decisions or actions, given their states when the state and action spaces are small.
Deep Reinforcement Learning for Time Scheduling in RF-Powered Backscatter Cognitive Radio Networks
To coordinate the transmission of multiple secondary transmitters, the secondary gateway needs to schedule the backscattering time, energy harvesting time, and transmission time among them.
Optimal Auction For Edge Computing Resource Management in Mobile Blockchain Networks: A Deep Learning Approach
However, a mechanism needs to be designed for edge resource allocation to maximize the revenue for the Edge Computing Service Provider and to ensure incentive compatibility and individual rationality is still open.
Computer Science and Game Theory
