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EdgeSlice: Slicing Wireless Edge Computing Network with Decentralized Deep Reinforcement Learning
5G and edge computing will serve various emerging use cases that have di...
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Deep Reinforcement Learning Based Mode Selection and Resource Allocation for Cellular V2X Communications
Cellular vehicle-to-everything (V2X) communication is crucial to support...
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Deep Reinforcement Learning Architecture for Continuous Power Allocation in High Throughput Satellites
In the coming years, the satellite broadband market will experience sign...
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Generalizable Resource Allocation in Stream Processing via Deep Reinforcement Learning
This paper considers the problem of resource allocation in stream proces...
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DeepPR: Incremental Recovery for Interdependent VNFs with Deep Reinforcement Learning
The increasing reliance upon cloud services entails more flexible networ...
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Deep Reinforcement Learning for Network Slicing with Heterogeneous Resource Requirements and Time Varying Traffic Dynamics
Efficient network slicing is vital to deal with the highly variable and ...
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GAN-based Deep Distributional Reinforcement Learning for Resource Management in Network Slicing
Network slicing is a key technology in 5G communications system, which a...
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DeepSlicing: Deep Reinforcement Learning Assisted Resource Allocation for Network Slicing
Network slicing enables multiple virtual networks run on the same physical infrastructure to support various use cases in 5G and beyond. These use cases, however, have very diverse network resource demands, e.g., communication and computation, and various performance metrics such as latency and throughput. To effectively allocate network resources to slices, we propose DeepSlicing that integrates the alternating direction method of multipliers (ADMM) and deep reinforcement learning (DRL). DeepSlicing decomposes the network slicing problem into a master problem and several slave problems. The master problem is solved based on convex optimization and the slave problem is handled by DRL method which learns the optimal resource allocation policy. The performance of the proposed algorithm is validated through network simulations.
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