A Handover Management Strategy Using Residence Time Prediction in 5G Ultra-Dense Networks

Arafah²

2023

IEEE Access

Heterogeneous ultra-dense networks (HUDNs) are one of the key enabling technologies for the fifth-generation (5G) networks. They aim to provide high capacity, low installation cost, and distributed traffic loads. The cell selection is a challenging issue in HUDNs, due to the different characteristics of base stations (BSs) and the existence of a large number of them. Thus, the traditional cell selection scheme is not applicable in such a network. In this paper, a novel adaptive cell selection strategy is proposed, called adaptive two-tier based on adaptive boosting (A2T-Boost). It can adapt to the various characteristics of base stations, as well as the different movement features of mobile stations such as vehicles and pedestrian. It is a software-defined networking (SDN)/machine learning (ML)-based scheme. A real-world case is considered in the downtown of Los Angeles city. Simulation results demonstrate that A2T-Boost achieves high prediction performance and it outperforms other related schemes in terms of average number of handovers (HOs) by up to 50%. Moreover, it enhances the average achievable downlink sum-rates and network energy efficiency achieved by vehicles by up to 33.76%. Furthermore, the average packet delay is decreased using the proposed scheme by up to 12.87%.

Section: A Non Ml-based Cell Selection Strategiesmentioning

confidence: 99%

“…HO RTP [38], and Zappone et al ANN-based [43] techniques, the performance of the new A2T-Boost is evaluated. In addition, Kapoor et al cell selection approaches [23], which are MD-VD, ML-VD, NN-S and NN-O, are compared to our proposed scheme.…”

Section: Ee(bits/joule) =mentioning

confidence: 99%

A2T-Boost: An Adaptive Cell Selection Approach for 5G/SDN-Based Vehicular Networks

Arafah²

2023

IEEE Access

“…In [42], a user association method, known as RTP), was developed by Qin et al for 5G ultra-dense networks. It selects base stations based on estimating the residence time inside a cell, where a predefined time threshold is set.…”

Section: Related Workmentioning

confidence: 99%

“…In this section, the simulation results are presented and discussed. The performance of the proposed A2T-KNN is compared with the traditional max-RSSI, HO RTP [42], and Zappone et al ANN-based [34] schemes. Table 7 displays the simulation parameters used to evaluate the cell selection schemes.…”

Section: Volume 15 2021mentioning

confidence: 99%

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

Arafah

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.

“…Qin et al introduced a cell selection strategy for 5G ultra-dense networks in [ 36 ]. It is called Handover based on Resident Time Prediction (HO RTP) and it aims to estimate the residence time inside a cell and select the base station that has the strongest RSSI value with a residence period longer than a predefined threshold.…”

Section: Related Workmentioning

confidence: 99%

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

Arafah

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.