Hidden Markov Model Based Machine Learning for mMTC Device Cell Association in 5G Networks

Balapuwaduge, Indika A. M.; Li, Frank Y.

doi:10.1109/icc.2019.8761913

Cited by 9 publications

(3 citation statements)

References 12 publications

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“…In [44], two hidden Markov-model (HMM) based ML techniques suggested by Balapuwaduge et al were introduced to address a cell selection issue. The key objectives of the suggested HMM-based ML methods were the accessibility and reliability of network resources.…”

Section: B Ml-based Cell Selection Strategiesmentioning

confidence: 99%

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

Alablani

Arafah²

2023

IEEE Access

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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%.

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Section: B Ml-based Cell Selection Strategiesmentioning

confidence: 99%

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

Alablani

Arafah²

2023

IEEE Access

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“…As a result, the probability of triggering a cell selection decision was reduced. The problem of cell selection was discussed in [27]. The proposed scheme presented two Hidden Markov Model (HMM)-based Machine Learning (ML) algorithms.…”

Section: Related Workmentioning

confidence: 99%

A Fairness-based Cell Selection Mechanism for Ultra-Dense Networks (UDNs)

Alotaibi

2023

Eng. Technol. Appl. Sci. Res.

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A typical 5G Ultra-Dense Network (UDN) comprises different types of Base Stations (BSs) in its structure. Dense deployment of small-cell BSs within a macrocell BS's coverage offers significant benefits, as the distance between a User Equipment (UE) and its small-cell BS is shorter with robust signals. Thus, the network capacity will increase dramatically. However, selecting an appropriate small-cell BS for a particular UE becomes a challenge in 5G UDNs. This study proposed a mechanism to address the cell selection problem and maximize fairness among UEs when making the cell selection decision. The proposed mechanism considered different parameters. The load balance for each small-cell BS was considered to fairly distribute UEs and avoid traffic congestion. Moreover, the signal strength was considered with the achievable data rate for all small-cell BSs to stimulate idle small-cell BSs to be in operating mode. A simulation was carried out in MATLAB to evaluate the proposed mechanism. Signal-to-Interference-Ratio (SINR) and Signal Strength (SS) -based strategies were also simulated for comparison. The proposed solution outperformed the other schemes in terms of fairness, as the UEs attached to the system were fairly distributed among small-cell BSs. Furthermore, the proposed mechanism achieved the best radio resource distribution in terms of fairness compared to the two other schemes.

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“…In [ 41 ], a cell selection issue was solved by introducing two hidden Markov-model- (HMM) based ML strategies that were proposed by Balapuwaduge et al The reliability and availability of network resources were the main targets of the proposed HMM-based ML schemes. Simulation results showed the superiority of the proposed strategies compared with a random cell selection method in terms of channel availability and reliability.…”

Section: Related Workmentioning

confidence: 99%

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

Alablani

Arafah

2021

Sensors

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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.

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Hidden Markov Model Based Machine Learning for mMTC Device Cell Association in 5G Networks

Cited by 9 publications

References 12 publications

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

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

A Fairness-based Cell Selection Mechanism for Ultra-Dense Networks (UDNs)

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

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