Machine Learning Techniques for 5G and Beyond

Kaur, Jasneet; Khan, M. Arif; Iftikhar, Mohsin; Imran, Muhammad; Qazi, Emad-ul-Haq

doi:10.1109/access.2021.3051557

Cited by 161 publications

(77 citation statements)

References 31 publications

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“…Integration of network performance with artificial intelligence [310] is the milestone to transform 5G into 6G. A promising approach might be clustering, one of the machine learning methods, to divide data flows, users, and services into clusters with similar properties to manage them more effectively.…”

Section: Summary and Future Workmentioning

confidence: 99%

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Verticals in 5G MEC-Use Cases and Security Challenges

et al. 2021

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5G is the fifth-generation cellular network satisfying the requirements IMT-2020 (International Mobile Telecommunications-2020) of the International Telecommunication Union. Mobile network operators started using it worldwide in 2019. Generally, 5G achieves exceptionally high values of performance parameters of access and transmission. The application of edge servers facilitated the implementation of such requirements of 5G, which resulted in 5G MEC (Multi-access Edge Computing) technology. Moreover, to optimize services for specific business applications, the concept of 5G vertical industries has been proposed. In this paper, we study how the application of the MEC technology affects the functioning of 5G MEC-based services. We consider twelve representative vertical industries of 5G MEC by presenting their essential characteristics, threats, vulnerabilities, and known attacks. Next, we analyze their functional properties, give efficiency patterns and identify the effect of applying the MEC technology in 5G on the resultant network's quality parameters to identify the expected security requirements. Finally, we identify the impact of classified threats on the 5G empowered vertical industries and identify the most sensitive cases to focus on their protection against network attacks in the first place.

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Section: Summary and Future Workmentioning

confidence: 99%

“…Regression techniques and neural networks could also be used to obtain expected resource utilization upfront and avoid an unacceptable level of resource utilization. Moreover, active methods from the AI toolset should be considered to increase the efficiency of the ML system by providing feedback [310].…”

Section: Summary and Future Workmentioning

confidence: 99%

Verticals in 5G MEC-Use Cases and Security Challenges

et al. 2021

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“…In the signaling performed within the core network (CN), a property of SDN, should be optimized for handover and re-routing purposes enabling more better support, scalability and performance to human mobility. Moreover, a faster and efficient CN signaling is only possible using AI techniques like machine learning [88]. Here, by efficiency, we imply that CN signalling presents an optimized and reduced cost in transmission, processing, and other related metrics.…”

Section: Open Challenges and Human Mobility Research Directionsmentioning

confidence: 99%

Mobile Networks and Internet of Things Infrastructures to Characterize Smart Human Mobility

2021

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The evolution of Mobile Networks and Internet of Things (IoT) architectures allows one to rethink the way smart cities infrastructures are designed and managed, and solve a number of problems in terms of human mobility. The territories that adopt the sensoring era can take advantage of this disruptive technology to improve the quality of mobility of their citizens and the rationalization of their resources. However, with this rapid development of smart terminals and infrastructures, as well as the proliferation of diversified applications, even current networks may not be able to completely meet quickly rising human mobility demands. Thus, they are facing many challenges and to cope with these challenges, different standards and projects have been proposed so far. Accordingly, Artificial Intelligence (AI) has been utilized as a new paradigm for the design and optimization of mobile networks with a high level of intelligence. The objective of this work is to identify and discuss the challenges of mobile networks, alongside IoT and AI, to characterize smart human mobility and to discuss some workable solutions to these challenges. Finally, based on this discussion, we propose paths for future smart human mobility researches.

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“…Depending on the output type, supervised learning can be divided as classification (discrete output) or regression (continuous output). K-nearest neighbors (KNN), support vector machines (SVM) and logistic regression (LR) are three of the most extended algorithms for supervised learning [20]. In unsupervised learning, no labeled data is provided and the aim of the learning agent is to find hidden features or structure of the data.…”

Section: A Classification Of ML Techniquesmentioning

confidence: 99%

Emerging Tools for Link Adaptation on 5G NR and Beyond: Challenges and Opportunities

Martín-Vega¹,

Ruiz-Sicilia

Aguayo

et al. 2021

IEEE Access

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With the speeding up of the fifth generation (5G) new radio (NR) worldwide commercialization, one of the paramount questions for operators and vendors is how to optimize the radio links, considering the widely diverse scenarios envisioned. One of the key pillars of 5G has been an unprecedented flexibility on the configuration of the radio access network (RAN) on scenarios that include cellular, vehicular, and industrial networks among others. This flexibility has its main exponent on link adaptation (LA), which has evolved into a multi-domain technique where a plethora of parameters, like numerology, bandwidth part, radio frequency beam, power, modulation and coding scheme (MCS) or multiple antenna precoding can be adapted to the instantaneous link conditions. Although such enhancements open the door to a significant performance improvement, they also pose many challenges to LA optimization. In this article, we first present the signaling aspects of NR technology for multi-domain LA and the challenges that need to be faced. Then, we explore the latest advances on LA for wireless networks. We envision a combination of machine learning (ML) tools with multi-domain LA as a key enabler for 5G and beyond networks. Finally, we investigate emerging ML approaches for LA and present a promising application of ML for LA that is assessed with simulations. With this scheme, the training is performed at the network side to relieve the user equipment (UE) to do such a complex task. It is shown with simulations that our ML approach outperforms the well known outer loop link adaptation (OLLA) algorithm in terms of instantaneous block error rate (BLER), while reaching the same average spectral efficiency (SE). Interestingly, it is shown that the proposed scheme only requires 4 bits to represent the features used to train the model, which makes it suitable for implementation in real systems with limited feedback.INDEX TERMS 5G networks, link adaptation, machine learning, supervised learning.

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Machine Learning Techniques for 5G and Beyond

Cited by 161 publications

References 31 publications

Verticals in 5G MEC-Use Cases and Security Challenges

Verticals in 5G MEC-Use Cases and Security Challenges

Mobile Networks and Internet of Things Infrastructures to Characterize Smart Human Mobility

Emerging Tools for Link Adaptation on 5G NR and Beyond: Challenges and Opportunities

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