Cell selection scheme for handover reduction based on moving direction and velocity of UEs for 5G multi-layered radio access networks

2021

IEEE Access

Fifth-generation (5G) cellular networks are a promising technology to meet the rapid growth in wireless traffic. Small cells are critical in fulfilling the requirements of 5G networks. A heterogeneous ultra-dense network (HUDN) is an enabling technology consisting of several types of small cells to enhance the performance of 5G networks effectively. A critical issue of HUDN is the cell selection method because the traditional technique for cell selection is inapplicable in such a network. This study proposes a novel adaptive cell selection (ADA-CS) scheme. It adapts to various characteristics of HUDNs and vehicle movements. It performs six phases to select the best base station with which to be associated. Simulation results show that, with low-and medium-speed vehicles, the ADA-CS scheme outperforms the traditional protocol in terms of the average number of handovers by 42.39%. In addition, it is superior to some relevant recent schemes by up to 36.53%. The adaptation feature of the proposed protocol provides additional improvements regarding the average number of handovers with high-speed vehicles. Therefore, it achieves superiority in terms of the average number of handover failures and unnecessary handovers. In addition, the ADA-CS scheme enhances the average achievable downlink data rates and spectral efficiency per vehicle by 3.98% and 2.79% compared with the traditional and the relevant recent schemes.

Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

An Adaptive Cell Selection Scheme for 5G Heterogeneous Ultra-Dense Networks

2021

IEEE Access

“…In [32], Kishida et al proposed a cell selection scheme for 5G multi-tier Radio Access Networks (RANs). The selected cell is the cell that has the maximum signal-tointerference-plus-noise ratio (SINR) value and is located in the user's direction.…”

Section: Related Workmentioning

confidence: 99%

An SDN/ML-Based Adaptive Cell Selection Approach for HetNets: A Real-World Case Study in London, UK

2021

IEEE Access

Heterogeneous networks (HetNets) are one of the key enabling technologies for nextgeneration networks. They aim to provide high capacity, low installation cost, and distributed traffic loads. The cell selection issue is an open research problem in HetNets, due to the different characteristics of base stations and the existence of a large number of them. In this paper, a novel software-defined networking (SDN)/machine learning (ML)-based adaptive algorithm is proposed, called adaptive two-tier, based on the K-nearest neighbor (A2T-KNN) algorithm. It is designed for millimeter wave (mmWave)based HetNets and it has the ability to adapt to the various movement features of moving vehicles, as well as the different characteristics of the base stations. A real-world case is considered in the center of London. Simulation results demonstrate that A2T-KNN achieves high prediction performance in association with different vehicle features and configuration information. It outperforms other related schemes in terms of average number of handovers by up to 45.83%. Moreover, it was found to enhance the average achievable downlink data rate and network energy efficiency achieved by vehicles by up to 17.18% and 16.86%, respectively.

“…A cell selection approach was proposed by Kiishida et al in [ 32 ] for 5G multi-layered Radio Access Networks (RANs). It considers the direction and velocity of UE movement to reduce the number of frequent handovers.…”

Section: Related Workmentioning

confidence: 99%

Enhancing 5G Small Cell Selection: A Neural Network and IoV-Based Approach

2021

Sensors

The ultra-dense network (UDN) is one of the key technologies in fifth generation (5G) networks. It is used to enhance the system capacity issue by deploying small cells at high density. In 5G UDNs, the cell selection process requires high computational complexity, so it is considered to be an open NP-hard problem. Internet of Vehicles (IoV) technology has become a new trend that aims to connect vehicles, people, infrastructure and networks to improve a transportation system. In this paper, we propose a machine-learning and IoV-based cell selection scheme called Artificial Neural Network Cell Selection (ANN-CS). It aims to select the small cell that has the longest dwell time. A feed-forward back-propagation ANN (FFBP-ANN) was trained to perform the selection task, based on moving vehicle information. Real datasets of vehicles and base stations (BSs), collected in Los Angeles, were used for training and evaluation purposes. Simulation results show that the trained ANN model has high accuracy, with a very low percentage of errors. In addition, the proposed ANN-CS decreases the handover rate by up to 33.33% and increases the dwell time by up to 15.47%, thereby minimizing the number of unsuccessful and unnecessary handovers (HOs). Furthermore, it led to an enhancement in terms of the downlink throughput achieved by vehicles.