Artificial Intelligence in 5G Technology: A Survey

Morocho-Cayamcela, Manuel Eugenio; Lim, Wansu

doi:10.1109/ictc.2018.8539642

Cited by 73 publications

(31 citation statements)

References 15 publications

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“…Given that cellular networks have been gaining popularity and getting more complex from one generation to another (Gupta and Jha, 2015;Agiwal et al, 2016;Shafi et al, 2017;Busari et al, 2018;Morocho Cayamcela and Lim, 2018;Ericsson, 2019), cognition (intelligence) is required to address the ever-growing demands in more challenging scenarios.…”

Section: How Can Artificial Intelligence Enable Son During the Covid-mentioning

confidence: 99%

“…Data is the main ingredient for intelligence, and fortunately, the amount of data generated by next generations of cellular networks will be much higher due to: (i) the prevalence of IoT devices; (ii) more BS deployments with network densification; (iii) more data transmitted by users; etc. (Xu et al, 2014;Agiwal et al, 2016;Bera et al, 2017;Lee et al, 2017;Akpakwu et al, 2018;Morocho Cayamcela and Lim, 2018). Learning, on the other hand, plays a crucial role in exploiting the huge amount of data, which subsequently enables cognitive networking.…”

Section: How Can Artificial Intelligence Enable Son During the Covid-mentioning

confidence: 99%

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The Role of Artificial Intelligence Driven 5G Networks in COVID-19 Outbreak: Opportunities, Challenges, and Future Outlook

et al. 2020

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Section: How Can Artificial Intelligence Enable Son During the Covid-mentioning

confidence: 99%

Section: How Can Artificial Intelligence Enable Son During the Covid-mentioning

confidence: 99%

The Role of Artificial Intelligence Driven 5G Networks in COVID-19 Outbreak: Opportunities, Challenges, and Future Outlook

et al. 2020

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“…Fifth generation (5G) networks require the application of machine learning techniques to operate effectively. Solving issues related to 5G wireless technology is an open direction for future research [ 29 ].…”

Section: Introductionmentioning

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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“…Machine learning has a considerable place in optimizing wireless communications networks [9][10][11][12][13], due to its strong capabilities in terms of convergence, dynamism, and agility. Moreover, it is envisioned that it will play rather a more critical role in the upcoming generations of mobile communication networks, such as sixth generation of mobile communication (6G) [14,15].…”

Section: Introductionmentioning

confidence: 99%

Multi-Objective UAV Positioning Mechanism for Sustainable Wireless Connectivity in Environments with Forbidden Flying Zones

et al. 2021

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A communication system based on unmanned aerial vehicles (UAVs) is a viable alternative for meeting the coverage and capacity needs of future wireless networks. However, because of the limitations of UAV-enabled communications in terms of coverage, energy consumption, and flying laws, the number of studies focused on the sustainability element of UAV-assisted networking in the literature was limited thus far. We present a solution to this problem in this study; specifically, we design a Q-learning-based UAV placement strategy for long-term wireless connectivity while taking into account major constraints such as altitude regulations, nonflight zones, and transmit power. The goal is to determine the best location for the UAV base station (BS) while reducing energy consumption and increasing the number of users covered. Furthermore, a weighting method is devised, allowing energy usage and the number of users served to be prioritized based on network/battery circumstances. The suggested Q-learning-based solution is contrasted to the standard k-means clustering method, in which the UAV BS is positioned at the centroid location with the shortest cumulative distance between it and the users. The results demonstrate that the proposed solution outperforms the baseline k-means clustering-based method in terms of the number of users covered while achieving the desired minimization of the energy consumption.

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Artificial Intelligence in 5G Technology: A Survey

Cited by 73 publications

References 15 publications

The Role of Artificial Intelligence Driven 5G Networks in COVID-19 Outbreak: Opportunities, Challenges, and Future Outlook

The Role of Artificial Intelligence Driven 5G Networks in COVID-19 Outbreak: Opportunities, Challenges, and Future Outlook

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

Multi-Objective UAV Positioning Mechanism for Sustainable Wireless Connectivity in Environments with Forbidden Flying Zones

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