Deep Q-learning for 5G network slicing with diverse resource stipulations and dynamic data traffic

Abstract

5G wireless networks use the network slicing technique that provides a suitable network to a service requirement raised by a network user. Further, the network performs effective slice management to improve the throughput and massive connectivity along with the required latency towards an appropriate resource allocation to these slices for service requirements. This paper presents an online Deep Q-learning based network slicing technique that considers a sigmoid transformed Quality of Experience, price satisfaction, and spectral efficiency as the reward function for bandwidth allocation and slice selection to serve the network users. The Next Generation Mobile Network (NGMN) vertical use cases have been considered for the simulations which also deals with the problem of international roaming and diverse intra-use case requirement variations by using only three standard network service slices termed as enhanced Mobile Broadband (eMBB), Ultra Reliable Low Latency Communication (uRLLC), and massive Machine Type Communication (mMTC). Our Deep Q-Learning model also converges significantly faster than the conventional Deep Q-Learning based approaches used in this field. The environment has been prepared based on ITU specifications for eMBB, uRLLC, mMTC. Our proposed method demonstrates a superior Quality-of-experience for the different users and the higher network bandwidth efficiency compared to the conventional slicing technique.