Consideration On Automation of 5G Network Slicing with Machine Learning

Kafle, Ved P.; Fukushima, Yusuke; Martínez-Juliá, Pedro; Miyazawa, Tatsuo

doi:10.23919/itu-wt.2018.8597639

Cited by 76 publications

(54 citation statements)

References 7 publications

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“…We believe that ML and data-driven approaches in general have a lot to offer to all aspects of the communication network architecture, and they have already started to have impact on the higher layers [98]- [100]. Yet, to realize this promise, significant research efforts are needed, from adaptation of existing ML techniques to the development of new ones that can meet the constraints and requirements of communication networks, including the implementation of at least some of these capabilities in low-power chips that can be used in mobile devices [101], [102], and/ or the development of fully distributed, yet efficient implementations that can employ low-power low-complexity mobile devices.…”

Section: Discussionmentioning

confidence: 99%

Machine Learning in the Air

Gündüz

Kerret

Sidiropoulos

et al. 2019

IEEE J. Select. Areas Commun.

199

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Thanks to the recent advances in processing speed, data acquisition and storage, machine learning (ML) is penetrating every facet of our lives, and transforming research in many areas in a fundamental manner. Wireless communications is another success story -ubiquitous in our lives, from handheld devices to wearables, smart homes, and automobiles. While recent years have seen a flurry of research activity in exploiting ML tools for various wireless communication problems, the impact of these techniques in practical communication systems and standards is yet to be seen. In this paper, we review some of the major promises and challenges of ML in wireless communication systems, focusing mainly on the physical layer. We present some of the most striking recent accomplishments that ML techniques have achieved with respect to classical approaches, and point to promising research directions where ML is likely to make the biggest impact in the near future. We also highlight the complementary problem of designing physical layer techniques to enable distributed ML at the wireless network edge, which further emphasizes the need to understand and connect ML with fundamental concepts in wireless communications.

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Section: Discussionmentioning

confidence: 99%

Machine Learning in the Air

Gündüz

Kerret

Sidiropoulos

et al. 2019

IEEE J. Select. Areas Commun.

199

View full text Add to dashboard Cite

show abstract

“…Using NLP can intensively reduce network traffic and improve the Quality of Service desired. Towards another approach and to optimize the network, reinforcement learning methods can be used as well to decrease the number of resources and determine the value of parameters for an optimal network slice setup [61]. Latency Optimization: Predicting computational resources based on historical data using machine learning will permit the network to schedule the resources in advance, hence reducing global latency.…”

Section: Area Of Use Description Solution Referencesmentioning

confidence: 99%

“…While heterogeneous learning models can be used to identify the unused spectral slot, elect from it a sub-channel and configure the terminals [63]. Other methods, including Support Vector Machine (SVM), gradient boosting decision tree, and spectral clustering, can be used as well to meet latency and bandwidth requirements for 5G [61]. These methods will noticeably reduce the latency and enhance the Quality of Service.…”

Section: Area Of Use Description Solution Referencesmentioning

confidence: 99%

Block5GIntell: Blockchain for AI-Enabled 5G Networks

et al. 2020

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Nowadays, 5G network is considered to be one of the main pillars of various industries, including the Internet of Things (IoT), smart cities, virtual reality, and many more. Unlike previous network generations, 5G utilizes complex digital technologies such as massive Multiple Input Multiple Output (mMIMO) and runs over higher radio frequencies. The introduction of new technologies and advanced features in the 5G network raises new challenges for network operators, and merging Artificial Intelligence (AI) is one of the effective solutions to address these complexities. However, AI-enabled 5G network engenders security concerns and requires improvement to meet the standardization and qualification of the new network generation. To mitigate these dilemmas, Blockchain must be integrated. Blockchain, as a decentralized methodology provides a secure sharing of information and resources among various nodes of 5G environments. Blockchain can support other technologies, such as AI-based 5G, to create smarter, more efficient, and secure cellular networks. In this paper, we present a comprehensive intelligence and secure data analytics framework for 5G networks based on the convergence of Blockchain and AI named "Block5GIntell". We depict the applications of Blockchain and AI on 5G networks separately and we argue on the support that Blockchain can provide for AI to create smart and secure 5G networks relying on our proposed framework. To support our proposition, we present an energy-saving case study using Blockchain for AI-enabled 5G. The simulation shows an overall 20% decrease in energy consumption at the RAN level.

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“…Besides these KPIs, a 5G network needs to be reliable, supports up to 500 km/h mobility for a device, and 10 6 devices in a square kilometer. Moreover, consuming up to 100 times less energy compared to LTE and having area traffic capacity up to 10 Mbit/s/m 2 are other improvements [4,7,8,9].…”

Section: Introductionmentioning

confidence: 99%

“…The importance of network slicing becomes obvious when realized that different use cases of 5G require various resources, and the capacity needed by the end-users must be delivered efficiently based on the requisites. For example, eMBB demands high bandwidth, mMTC needs ultra-dense connectivity, and for meeting URLLC necessities, providing low latency is paramount [8,13,14]. These unique features make 5G capable of delivering new services to Industrial IoTs, TSCs (Time-Sensitive Communications), NPNs (Non-Public Networks) [5], reliable communication between vehicles [15], novel services to industrial stakeholders (i.e., vertical industries) [16], location-based services [17], and NB-IoTs [18].…”

Section: Introductionmentioning

confidence: 99%

Challenges on the Way of Implementing TCP Over 5G Networks

Poorzare

Calveras

2020

IEEE Access

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5G cellular communication, especially with its hugely available bandwidth provided by millimeter-wave, is a promising technology to fulfill the coming high demand for vast data rates. These networks can support new use cases such as Vehicle to Vehicle and augmented reality due to its novel features such as network slicing along with the mmWave multigigabit-per-second data rate. Nevertheless, 5G cellular networks suffer from some shortcomings, especially in high frequencies because of the intermittent nature of channels when the frequency rises. Non-line of sight state, is one of the significant issues that the new generation encounters. This drawback is because of the intense susceptibility of higher frequencies to blockage caused by obstacles and misalignment. This unique characteristic can impair the performance of the reliable transport layer widely deployed protocol, TCP, in attaining high throughput and low latency throughout a fair network. As a result, the protocol needs to adjust the congestion window size based on the current situation of the network. However, TCP is not able to adjust its congestion window efficiently, and it leads to throughput degradation of the protocol. This paper presents a comprehensive analysis of reliable end-to-end communications in 5G networks. It provides the analysis of the effects of TCP in 5G mmWave networks, the discussion of TCP mechanisms and parameters involved in the performance over 5G networks, and a survey of current challenges, solutions, and proposals. Finally, a feasibility analysis proposal of machine learning-based approaches to improve reliable end-to-end communications in 5G networks is presented. INDEX TERMS 5G, end-to-end reliability, mmWave, TCP ANNA CALVERAS AUGÉ was born in Barcelona, Spain, in 1969. She obtained a Ph.D. in Telecommunications Engineering from the Universitat Politècnica de Catalunya, Spain, in 2000. She is an associate professor at the mentioned University, at the Computer Networks Department, in the Wireless Networks Group (WNG). Her research interests and expertise areas comprise the design, evaluation, and optimization of communications protocols and architectures for cellular, wireless multihop networks, ad-hoc networks, wireless sensor networks, the Internet of Things, and application domains such as smart cities, building automation, satellite and emergency environments. She has been involved in several National and International research or technology transfer projects, and she has published in International and National conferences and journals.

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Consideration On Automation of 5G Network Slicing with Machine Learning

Cited by 76 publications

References 7 publications

Machine Learning in the Air

Machine Learning in the Air

Block5GIntell: Blockchain for AI-Enabled 5G Networks

Challenges on the Way of Implementing TCP Over 5G Networks

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