Abstract

The fifth generation (5G) wireless technology emerged with marvelous effort to state, design, deployment and standardize the upcoming wireless network generation. Artificial intelligence (AI) and machine learning (ML) techniques are well capable to support 5G latest technologies that are expected to deliver high data rate to upcoming use cases and services such as massive machine type communications (mMTC), enhanced mobile broadband (eMBB), and ultra-reliable low latency communications (uRLLC). These services will surely help Gbps of data within the latency of few milliseconds in Internet of Things paradigm. This survey presented 5G mobility management in ultra-dense small cells networks using reinforcement learning techniques. First, we discussed existing surveys then we are focused on handover (HO) management in ultra-dense small cells (UDSC) scenario. Following, this study also discussed how machine learning algorithms can help in different HO scenarios. Nevertheless, future directions and challenges for 5G UDSC networks were concisely addressed.

Highlights

  • Over the recent years, wireless technology is boosted with potential capabilities through research and innovation

  • In resource control (RRC) inactive mode, the content of user equipment access stratum is stored in both core network and UE, whereas the connection between radio access network and core network is remained active to avoid power consumption along with the control plane delay

  • When both the second and third stage of HO is operated by mobile station or any other controller, HO process is classified according to controller basis such as network controlled HO (NCHO), mobile controlled HO (MCHO) and mobile assisted HO (MAHO) [74]

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Summary

Introduction

Wireless technology is boosted with potential capabilities through research and innovation. The exponential increment of various wireless devices, more usage of data, improved quality of service, and the expansion of cellular network have gained importance. Main drivers are exponential increment of various wireless devices, data hunger applications, and providing improved quality of service/experience that required expansion of cellular network to support upcoming 5G use cases. This evolution of wireless networks include high speed in gigabits per sec, low latency, high throughput, and better efficiency of spectrum in contrast to 4G-LTE networks [1].

Motivation
Contribution
Road Map of the Survey
Related Surveys
Overview of HO Management in 5G UDSC Network
Mobility Management in 5G UDSC Network
HO Management in 5G UDSC Network
Classification of HO Types
Reinforcement Learning Algorithms for HO
Reinforcement Learning
Related Contributions
Types of Reinforcement Learning
Challenges and Future Research Directions
Findings
Conclusions
Full Text
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