Neighbor Cell List Optimization in Handover Management Using Cascading Bandits Algorithm

Chao Wang,Jian Yang,Ruida Zhou,Xiaofeng Jiang,Huasen He,Shuangwu Chen

doi:10.1109/access.2020.3011015

Abstract

Frequent handover is a key challenge in 5G Ultra-Dense Networks (UDN). In this paper, we show the significance of configuring Neighbor Cell List (NCL) in handover procedure. To cope with the high dynamic of UDN, we propose an online-learning method, namely the Cost-aware Cascading Bandits NCL configuration (CCB-NCL) algorithm, which applies the cascading model and Multi-Armed Bandits (MAB) theory to configure the efficient Neighbor Cell List (eNCL) and improves the handover performance by assisting the User Equipment (UE) to choose the optimal target Base Station (BS). We provide rigorous proof of regret bound to show the asymptotic convergence of the proposed CCB-NCL algorithm. The robustness and efficiency of the proposed algorithm are both demonstrated in different network scenarios, where varies BS densities, BS dynamic and network heterogeneity are considered respectively. In the simulation work, we reproduce two existing methods of configuring NCL in handover management, named dynamic threshold based solution and received signal strength based solution. In comparison with the existing solutions, the proposed algorithm can reduce the overlarge signaling cost and unnecessary delay in the preparation phase of handover procedure by significantly shortening the length of NCLs and reducing the number of scanned BSs. Extensive simulations are conducted in different scenarios to validate the robustness of the proposed algorithm and the results show that the proposed CCB-NCL algorithm is a superior approach to efficient handover management.

Highlights

To meet the growing demand of high data rate, low latency and high energy-efficiency in wireless networks, many new technologies have been proposed and applied in the fifth generation wireless communication (5G) [1]
2) We model the scanning latency as cost to the value function in the online learning process, and force the efficient Neighbor Cell List (eNCL) to reduce the latency in handover preparation phase by avoiding unnecessary Base Station (BS) scans
SYSTEM MODEL In this paper, we consider an ultra dense cellular network, which contains a set of BSs and a random number of User Equipments (UE) distributed according to a homogeneous Poisson Point Process (PPP) with intensity λP

Summary

INTRODUCTION

To meet the growing demand of high data rate, low latency and high energy-efficiency in wireless networks, many new technologies have been proposed and applied in the fifth generation wireless communication (5G) [1]. This information was provided by the measurement reports from the corresponding UE These handover management schemes considered only RSS for making the handover decision, without considering potential overloading of the target BSs, which can be optimized in configuration of NCL to enhance the handover performance [4]. CONTRIBUTIONS AND STRUCTURE In our work, we consider both RSS and cell load of potential target BSs and use a learning method based on the long-term performance to configure the optimal NCL, which can avoid some unnecessary scans of BSs in NCL. The notation Pr{·} is used to denote the probability of some events

SYSTEM MODEL

NCL OPTIMIZATION ALGORITHM DESIGN

CCB-NCL ALGORITHM

Findings

CONCLUSION