面向B5G多业务场景基于D3QN的双时间尺度网络切片算法

doi:10.11959/j.issn.1000-436x.2022207

Abstract

Abstract:

To effectively meet the differentiated quality of service (QoS) requirements of different slices, a dual time scale network slicing resource allocation algorithm based on dueling double DQN (D3QN) was proposed for B5G multi-service scenarios.The joint resource slicing and scheduling problem was formulated, with the weighted sum of the normalized spectral efficiency (SE) and the QoS of users indices of different slices as the optimization objective.On large time scale, the SDN controller used the D3QN algorithm to pre-allocate resources to different slices based on the resource requirements of each service, and then performed BS-level resource updating based on the load status of BS.On small time scale, the BS scheduled resources to end-users by using the round-robin scheduling algorithm.The simulation results show that the proposed algorithm has excellent performance in ensuring the QoS requirements of slice users, SE and system utility.Compared with the other 4 baseline algorithms, the system utility is improved by 3.22%, 3.81%, 7.48% and 21.14%, respectively.

Key words: dual time scale, resource allocation, network slicing, dueling double DQN, system utility

CLC Number:

TN929.5

Geng CHEN, Shuhu QI, Fei SHEN, Qingtian ZENG. Dual time scale network slicing algorithm based on D3QN for B5G multi-service scenarios[J]. Journal on Communications, 2022, 43(11): 213-224.

Figures/Tables 14

References 24

[1]	DOGRA A , JHA R K , JAIN S . A survey on beyond 5G network with the advent of 6G:architecture and emerging technologies[J]. IEEE Access, 2021,9: 67512-67547.
[2]	MLIKA Z , CHERKAOUI S . Massive IoT access with NOMA in 5G networks and beyond using online competitiveness and learning[J]. IEEE Internet of Things Journal, 2021,8(17): 13624-13639.
[3]	ELAYOUBI S E , JEMAA S B , ALTMAN Z ,et al. 5G RAN slicing for verticals:enablers and challenges[J]. IEEE Communications Magazine, 2019,57(1): 28-34.
[4]	FILALI A , ABOUAOMAR A , CHERKAOUI S ,et al. Multi-access edge computing:a survey[J]. IEEE Access, 2020,8: 197017-197046.
[5]	SHU Z G , TALEB T . A novel QoS framework for network slicing in 5G and beyond networks based on SDN and NFV[J]. IEEE Network, 2020,34(3): 256-263.
[6]	HUA Y X , LI R P , ZHAO Z F ,et al. GAN-powered deep distributional reinforcement learning for resource management in network slicing[J]. IEEE Journal on Selected Areas in Communications, 2020,38(2): 334-349.
[7]	LI R P , WANG C J , ZHAO Z F ,et al. The LSTM-based advantage actor-critic learning for resource management in network slicing with user mobility[C]// Proceedings of IEEE Communications Letters. Piscataway:IEEE Press, 2020: 2005-2009.
[8]	FILALI A , MLIKA Z , CHERKAOUI S ,et al. Dynamic SDN-based radio access network slicing with deep reinforcement learning for URLLC and eMBB services[J]. IEEE Transactions on Network Science and Engineering, 2022,9(4): 2174-2187.
[9]	SUN G L , XIONG K , BOATENG G O ,et al. Autonomous resource provisioning and resource customization for mixed traffics in virtualized radio access network[J]. IEEE Systems Journal, 2019,13(3): 2454-2465.
[10]	SHAO Y , LI R P , HU B ,et al. Graph attention network-based multi-agent reinforcement learning for slicing resource management in dense cellular network[J]. IEEE Transactions on Vehicular Technology, 2021,70(10): 10792-10803.
[11]	ABIKO Y , SAITO T , IKEDA D ,et al. Flexible resource block allocation to multiple slices for radio access network slicing using deep reinforcement learning[J]. IEEE Access, 2020,8: 68183-68198.
[12]	YANG G , LIU Q , ZHOU X W ,et al. Two-tier resource allocation in dynamic network slicing paradigm with deep reinforcement learning[C]// Proceedings of 2019 IEEE Global Communications Conference. Piscataway:IEEE Press, 2019: 1-6.
[13]	WU H Q , CHEN J Y , ZHOU C H ,et al. Learning-based joint resource slicing and scheduling in space-terrestrial integrated vehicular networks[J]. Journal of Communications and Information Networks, 2021,6(3): 208-223.
[14]	SHI W S , LI J L , YANG P ,et al. Two-level soft RAN slicing for customized services in 5G-and-beyond wireless communications[J]. IEEE Transactions on Industrial Informatics, 2022,18(6): 4169-4179.
[15]	PAPA A , JANO A , AYVA?IK S ,et al. User-based quality of service aware multi-cell radio access network slicing[J]. IEEE Transactions on Network and Service Management, 2022,19(1): 756-768.
[16]	HU J Z , ZHENG Z J , DI B Y ,et al. Multi-layer radio network slicing for heterogeneous communication systems[J]. IEEE Transactions on Network Science and Engineering, 2020,7(4): 2378-2391.
[17]	SUN Y , QIN S , FENG G ,et al. Service provisioning framework for RAN slicing:user admissibility,slice association and bandwidth allocation[J]. IEEE Transactions on Mobile Computing, 2021,20(12): 3409-3422.
[18]	ZHAO N , LIANG Y C , NIYATO D ,et al. Deep reinforcement learning for user association and resource allocation in heterogeneous cellular networks[J]. IEEE Transactions on Wireless Communications, 2019,18(11): 5141-5152.
[19]	孙国林, 欧睿杰, 刘贵松 . 基于深度强化学习的应急物联网切片资源预留算法[J]. 通信学报, 2020,41(9): 8-20.
	SUN G L , OU R J , LIU G S . Deep reinforcement learning-based resource reservation algorithm for emergency Internet-of-things slice[J]. Journal on Communications, 2020,41(9): 8-20.
[20]	亓伟敬, 宋清洋, 郭磊 . 面向软件定义多模态车联网的双时间尺度RAN切片资源分配[J]. 通信学报, 2022,43(4): 60-70.
	QI W J , SONG Q Y , GUO L . Dual time scale resource allocation for RAN slicing in software-defined oriented polymorphic IoV[J]. Journal on Communications, 2022,43(4): 60-70.
[21]	喻鹏, 张俊也, 李文璟 ,等. 移动边缘网络中基于双深度 Q 学习的高能效资源分配方法[J]. 通信学报, 2020,41(12): 148-161.
	YU P , ZHANG J Y , LI W J ,et al. Energy-efficient resource allocation method in mobile edge network based on double deep Q-learning[J]. Journal on Communications, 2020,41(12): 148-161.
[22]	3GPP. Further advancements for E-UTRA physical layer aspects:TR36.814 (V9.0.0)[S]. 2010.
[23]	3GPP. Service requirements for 5G system (Release 15):TS 22.261 V18.3.0[S]. 2021.
[24]	SUN G L , XIONG K , BOATENG G O ,et al. Resource slicing and customization in RAN with dueling deep Q-network[J]. Journal of Network and Computer Applications, 2020,157:102573.

Metrics

Recommended 0

No Suggested Reading articles found!

参数	eMBB	URLLC	VoLTE
带宽/MHz	40	40	40
轮询调度/ms	0.5	0.5	0.5
用户数量	400	600	200
用户移动速度/(m·s^-1)	均匀分布[1,5 ]	均匀分布[6,10]	均匀分布[0,1]
数据包请求到达间隔分布	截断帕累托分布	指数分布	均匀分布
	[指数系数=1.2, mean=6 ms, max=12.5 ms]	[mean=180 ms]	[min= 0 ms, max=160 ms]
数据包的大小分布	截断帕累托分布	0.3 MB	40 B
	[指数系数=1.2, mean=100 B, max=250 B
切片要求速率	100 Mbit/s	10 Mbit/s	51 kbit/s
时延/ms	10	3	10

Dual time scale network slicing algorithm based on D3QN for B5G multi-service scenarios

RichHTML

PDF

Knowledge

Abstract

Cite this article

share this article

Figures/Tables 14

References 24

Related Articles 15

Metrics

Recommended 0

[1]	Li WANG, Aiguo FEI, Ping ZHANG, Lianming XU. Research on new frameworks and key technologies for intelligent emergency command communication networks [J]. Journal on Communications, 2023, 44(6): 1-11.
[2]	Zaijian WANG, Huimin GU. Network slicing resource allocation strategy based on joint optimization [J]. Journal on Communications, 2023, 44(5): 234-245.
[3]	Xueyong YU, Lixiang QIU, Jianing SONG, Hongbo ZHU. Security communication and energy efficiency optimization strategy in UAV-aided edge computing [J]. Journal on Communications, 2023, 44(3): 45-54.
[4]	Guojun LI, Xu HOU, Changrong YE, Yiping LUO. Wide area cooperative resource allocation algorithm for shortwave communication access network [J]. Journal on Communications, 2023, 44(2): 112-121.
[5]	Long LONG, Zichen LIU, Zaiwang LU, Yucheng ZHANG, Lei LI. Joint optimization strategy of service cache and resource allocation in mobile edge network [J]. Journal on Communications, 2023, 44(1): 64-74.
[6]	Xiaorong ZHU, Kang CHEN. Research on elastic handover algorithm of 6G network based on fine-grained slicing [J]. Journal on Communications, 2022, 43(9): 148-156.
[7]	Shaoshuai FAN, Jianbo WU, Hui TIAN. Federated learning resource management for energy-constrained industrial IoT devices [J]. Journal on Communications, 2022, 43(8): 65-77.
[8]	Li WANG, Qing WEI, Lianming XU, Yuan SHEN, Ping ZHANG, Aiguo FEI. Research on low-energy-consumption deployment of emergency UAV network for integrated communication-navigating-sensing [J]. Journal on Communications, 2022, 43(7): 1-20.
[9]	Peng QIN, Haoting HE, Xiongwen ZHAO, Yang FU, Yu ZHANG, Miao WANG, Shuo WANG, Xue WU. Efficient resource allocation with context-awareness for parked car road side unit-based Internet of vehicles [J]. Journal on Communications, 2022, 43(7): 113-125.
[10]	Jianxin LIAO, Xiaoyuan FU, Qi QI, Jingyu WANG, Haifeng SUN. 6G-ADM: knowledge based 6G network management and control architecture [J]. Journal on Communications, 2022, 43(6): 3-15.
[11]	Damin ZHANG, Yi WANG, Chengcheng ZOU, Peiwen ZHAO, Linna ZHANG. Resource allocation strategies for improved mayfly algorithm in cognitive heterogeneous cellular network [J]. Journal on Communications, 2022, 43(6): 156-167.
[12]	Weijing QI, Qingyang SONG, Lei GUO. Dual time scale resource allocation for RAN slicing in software-defined oriented polymorphic IoV [J]. Journal on Communications, 2022, 43(4): 60-70.
[13]	Zhengyu ZHU, Gengwang HOU, Chongwen HUANG, Gangcan SUN, Wanming HAO, Jing LIANG. Systems resource allocation algorithm for RIS-assisted D2D secure communication based on parallel CNN [J]. Journal on Communications, 2022, 43(3): 172-179.
[14]	Wei SUN, Qingyang SONG, Lei GUO. Resource allocation algorithm for intelligent reflecting surface-aided SWIPT networks [J]. Journal on Communications, 2022, 43(2): 34-43.
[15]	Yan SUN, Ao XIONG, Chengling JIANG, Wei WANG, Dongxiao YU, Shaoyong GUO. Blockchain-based computing and wireless communication resource joint management double auction model [J]. Journal on Communications, 2022, 43(11): 14-25.