DeepSlicing: Deep Reinforcement Learning Assisted Resource Allocation for Network Slicing

2020-08-17 · Qiang Liu, Tao Han, Ning Zhang, Ye Wang

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.

📄 PDF Abstract BibTeX arXiv:2008.07614

Code (0)

등록된 구현이 없습니다.

Tasks

Deep Reinforcement Learningreinforcement-learning Reinforcement Learning Reinforcement Learning (RL)

Intelligent Hybrid Resource Allocation in MEC-assisted RAN Slicing Network

2024-05-02 · Chong Zheng, Yongming Huang, Cheng Zhang, Tony Q. S. Quek

In this paper, we aim to maximize the SSR for heterogeneous service demands in the cooperative MEC-assisted RAN slicing system by jointly considering the multi-node computing resources cooperation and allocation, the tra…

Graph Neural Network reinforcement-learning Reinforcement Learning

T-s3ra: traffic-aware scheduling for secure slicing and resource allocation in sdn/nfv enabled 5g networks

2021-07-11 · Ali J. Ramadhan

Network slicing and resource allocation play pivotal roles in software-defined network (SDN)/network function virtualization (NFV)-assisted 5G networks. In 5G communications, the traffic rate is high, necessitating high …

Scheduling

Deep Reinforcement Learning for Combined Coverage and Resource Allocation in UAV-aided RAN-slicing

2022-11-15 · Lorenzo Bellone, Boris Galkin, Emiliano Traversi, Enrico Natalizio

Network slicing is a well assessed approach enabling virtualization of the mobile core and radio access network (RAN) in the emerging 5th Generation New Radio. Slicing is of paramount importance when dealing with the eme…

Deep Reinforcement Learning

Fast and Scalable Network Slicing by Integrating Deep Learning with Lagrangian Methods

2024-01-22 · Tianlun Hu, Qi Liao, Qiang Liu, Antonio Massaro 외

Network slicing is a key technique in 5G and beyond for efficiently supporting diverse services. Many network slicing solutions rely on deep learning to manage complex and high-dimensional resource allocation problems. H…

Deep Learning Deep Reinforcement Learning

Reinforcement Learning for Dynamic Resource Optimization in 5G Radio Access Network Slicing

2020-09-14 · Yi Shi, Yalin E. Sagduyu, Tugba Erpek

The paper presents a reinforcement learning solution to dynamic resource allocation for 5G radio access network slicing. Available communication resources (frequency-time blocks and transmit powers) and computational res…

Q-Learning reinforcement-learning Reinforcement Learning Reinforcement Learning (RL)