DeepSlicing: Deep Reinforcement Learning Assisted Resource Allocation for Network Slicing
This addresses resource allocation for network slicing in 5G, but it is incremental as it combines existing methods without a major breakthrough.
The authors tackled the problem of allocating diverse network resources to slices in 5G and beyond by proposing DeepSlicing, which integrates ADMM and deep reinforcement learning, and validated it through simulations.
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.