scholarly journals Machine Learning for Physical Layer in 5G and beyond Wireless Networks: A Survey

Electronics ◽  
2021 ◽  
Vol 11 (1) ◽  
pp. 121
Author(s):  
Jawad Tanveer ◽  
Amir Haider ◽  
Rashid Ali ◽  
Ajung Kim
Keyword(s):  
Machine Learning ◽  
Wireless Networks ◽  
Network Architecture ◽  
Spectrum Sharing ◽  
High Mobility ◽  
Physical Layer ◽  
Use Cases ◽  
Security And Privacy ◽  
Fifth Generation ◽  
5G Network

Fifth-generation (5G) technology will play a vital role in future wireless networks. The breakthrough 5G technology will unleash a massive Internet of Everything (IoE), where billions of connected devices, people, and processes will be simultaneously served. The services provided by 5G include several use cases enabled by the enhanced mobile broadband, massive machine-type communications, and ultra-reliable low-latency communication. Fifth-generation networks potentially merge multiple networks on a single platform, providing a landscape for seamless connectivity, particularly for high-mobility devices. With their enhanced speed, 5G networks are prone to various research challenges. In this context, we provide a comprehensive survey on 5G technologies that emphasize machine learning-based solutions to cope with existing and future challenges. First, we discuss 5G network architecture and outline the key performance indicators compared to the previous and upcoming network generations. Second, we discuss next-generation wireless networks and their characteristics, applications, and use cases for fast connectivity to billions of devices. Then, we confer physical layer services, functions, and issues that decrease the signal quality. We also present studies on 5G network technologies, 5G propelling trends, and architectures that help to achieve the goals of 5G. Moreover, we discuss signaling techniques for 5G massive multiple-input and multiple-output and beam-forming techniques to enhance data rates with efficient spectrum sharing. Further, we review security and privacy concerns in 5G and standard bodies’ actionable recommendations for policy makers. Finally, we also discuss emerging challenges and future directions.

scholarly journals Leveraging Machine-Learning for D2D Communications in 5G/Beyond 5G Networks

Electronics ◽  
2021 ◽  
Vol 10 (2) ◽  
pp. 169
Author(s):  
Sherief Hashima ◽  
Basem M. ElHalawany ◽  
Kohei Hatano ◽  
Kaishun Wu ◽  
Ehab Mahmoud Mohamed
Keyword(s):  
Machine Learning ◽  
Network Architecture ◽  
D2d Communication ◽  
Neighbor Discovery ◽  
Research Directions ◽  
Fifth Generation ◽  
Research Activities ◽  
Comprehensive Survey ◽  
Network Overhead

Device-to-device (D2D) communication is a promising paradigm for the fifth generation (5G) and beyond 5G (B5G) networks. Although D2D communication provides several benefits, including limited interference, energy efficiency, reduced delay, and network overhead, it faces a lot of technical challenges such as network architecture, and neighbor discovery, etc. The complexity of configuring D2D links and managing their interference, especially when using millimeter-wave (mmWave), inspire researchers to leverage different machine-learning (ML) techniques to address these problems towards boosting the performance of D2D networks. In this paper, a comprehensive survey about recent research activities on D2D networks will be explored with putting more emphasis on utilizing mmWave and ML methods. After exploring existing D2D research directions accompanied with their existing conventional solutions, we will show how different ML techniques can be applied to enhance the D2D networks performance over using conventional ways. Then, still open research directions in ML applications on D2D networks will be investigated including their essential needs. A case study of applying multi-armed bandit (MAB) as an efficient online ML tool to enhance the performance of neighbor discovery and selection (NDS) in mmWave D2D networks will be presented. This case study will put emphasis on the high potency of using ML solutions over using the conventional non-ML based methods for highly improving the average throughput performance of mmWave NDS.

scholarly journals Energy performance of 5G backhaul wireless network utilizing hybrid centric-distributed architecture

2019 ◽  
Vol 16 (3) ◽  
pp. 1343
Author(s):  
Hamza Mohammed Ridha Al-Khafaji ◽  
Hasan Shakir Majdi
Keyword(s):  
Energy Efficiency ◽  
Wireless Network ◽  
Network Architecture ◽  
Energy Performance ◽  
Small Cells ◽  
Network Architectures ◽  
Distributed Architecture ◽  
Wireless Backhaul ◽  
Fifth Generation ◽  
5G Network

<p>This paper scrutinizes the influence of deployment scenarios on the energy performance of fifth-generation (5G) network at various backhaul wireless frequency bands. An innovative network architecture, the hybrid centric-distributed, is employed and its energy efficiency (EE) model is analyzed. The obtained results confirm that the EE of the 5G network increases with an increasing number of small cells and degrades with an increasing frequency of wireless backhaul and radius of small cells regardless of the network architectures. Moreover, the hybrid centric-distributed architecture augments the EE when compared with the distributed architecture.</p>

Machine Learning in 5G Multimedia Communications

2021 ◽  
pp. 204-225
Author(s):  
Dragorad A. Milovanovic ◽  
Zoran S. Bojkovic ◽  
Dragan D. Kukolj
Keyword(s):  
Machine Learning ◽  
Wireless Networks ◽  
Deep Learning ◽  
Complex Networks ◽  
Future Research ◽  
Fifth Generation ◽  
Data Formats ◽  
Research Challenges ◽  
Wireless Environments ◽  
Special Case

Machine learning (ML) has evolved to the point that this technique enhances communications and enables fifth-generation (5G) wireless networks. ML is great to get insights about complex networks that use large amounts of data, and for predictive and proactive adaptation to dynamic wireless environments. ML has become a crucial technology for mobile broadband communication. Special case goes to deep learning (DL) in immersive media. Through this chapter, the goal is to present open research challenges and applications of ML. An exploration of the potential of ML-based solution approaches in the context of 5G primary eMBB, mMTC, and uHSLLC services is presented, evaluating at the same time open issues for future research, including standardization activities of algorithms and data formats.

scholarly journals A Real Case of Implementation of the Future 5G City

2018 ◽  
Vol 11 (1) ◽  
pp. 4 ◽  
Author(s):  
Dania Marabissi ◽  
Lorenzo Mucchi ◽  
Romano Fantacci ◽  
Maria Spada ◽  
Fabio Massimiani ◽  
...  
Keyword(s):  
Communication Systems ◽  
Social Impact ◽  
Field Trials ◽  
Use Cases ◽  
Wireless Communication Systems ◽  
Performance Requirements ◽  
Fifth Generation ◽  
Wide Range ◽  
5G Network ◽  
The Future

The fifth generation (5G) of wireless communication systems is considered the key technology to enable a wide range of application scenarios and the effective spreading of the smart city concept. Vertical business use cases, specifically designed for the future 5G city, will have a strong economical and social impact. For this reason, ongoing 5G field trials have to test newly deployed technologies as well as the capability of 5G to create a new digital economy. This paper describes the 5G field trial environment that was launched in Italy at the end of 2017. The aim is to evaluate the capability of the 5G network of supporting innovative services with reference to suitably designed key performance indicators and to evaluate the opportunities offered by these services. Indeed, vertical business use cases, specifically designed for the future 5G city, with a strong economic and social impact, are under implementation and will be evaluated. In particular, the paper provides a detailed description of the deployment of an actual complete integrated 5G network. It shows how 5G is effective enabling technology for a wide range of vertical business and use cases. Indeed, its flexibility allows to satisfy completely different performance requirements of real services. Some preliminary results, obtained during the first phase, are presented for a smart mobility scenario.

scholarly journals A Study on Massive Mimo 5G Challenges

2020 ◽  
Author(s):  
Kanchana Devi A ◽  
Bhuvaneswari B
Keyword(s):  
Network Architecture ◽  
Massive Mimo ◽  
Channel Model ◽  
High Reliability ◽  
High Energy ◽  
Base Station ◽  
High Signal ◽  
Fifth Generation ◽  
5G Network ◽  
Mimo Technology

Massive MIMO is a advance of MIMO technology. M-MIMO use hundreds of Base station (BS) to simultaneously serve multiple users. It combines with millimeter wave (mmWave) to provide huge spectral efficient, high reliability and high energy efficiency. Massive MIMO gives huge antennas, high signal strength, less noise reduction and also using better channel model. This paper discusses the detail description of fifth generation (5G) network architecture and to improve massive MIMO in existing technology.

Exploiting the physical layer for enhanced security [Security and Privacy in Emerging Wireless Networks

2010 ◽  
Vol 17 (5) ◽  
pp. 63-70 ◽  
Author(s):  
Suhas Mathur ◽  
Alex Reznik ◽  
Chunxuan Ye ◽  
Rajat Mukherjee ◽  
Akbar Rahman ◽  
...  
Keyword(s):  
Wireless Networks ◽  
Physical Layer ◽  
Security And Privacy

scholarly journals Smart Data Analytics for Machine Learning Approach in 5G Network

2021 ◽  
pp. 1-4
Author(s):  
Camilla Schaefer ◽  
Ana Makatsaria
Keyword(s):  
Machine Learning ◽  
Data Analytics ◽  
Large Range ◽  
Transmission Capacity ◽  
Use Cases ◽  
Learning Approach ◽  
5G Network ◽  
Machine Learning Approach ◽  
Smart Data ◽  
Smart Vehicle

Psychological radio innovation can possibly ameliorate the shortage of remote assets on the grounds that unlicensed clients can utilize remote assets just on the off chance that they no affect the tasks of authorized clients. Later, psychological radio CLOUD (CogCLOUD) will be built from numerous versatile SUs associated with one another in a circulated way, which can be sent for different applications, including smart vehicle frameworks. Notwithstanding, in CogCLOUD, channel exchanging is intrinsically important at whatever point an essential client with a permit shows up on the channel. Permitting optional clients to pick an accessible channel among a large range hence empowers dependable correspondence in this unique circumstance, yet correspondence qualities, for example, bottleneck transmission capacity, RTT would change with channel switch. Because of the change, TCP needs refresh the blockage window to utilize the accessible assets. TCP CRAHN was proposed for CogCLOUD. TCP CRAHN is first assessed in quite a while the bottleneck transmission capacity then RTT changes. Considering the outcomes, TCP CoBA is proposed to additionally increase the throughput of the above use cases. TCP CoBA refreshes the cwnd dependent on accessible cradle space in transfer hub upon channel switch, just as other correspondence attributes. Through recreations, we show that contrasted and TCP CRAHN, TCP CoBA increase the throughput by up to 200%.

Spectrum Sharing for D2D Communications in Fifth-Generation Wireless Networks

2019 ◽  
pp. 166-196
Author(s):  
Devendra Singh Gurjar ◽  
Prabhat Kumar Upadhyay
Keyword(s):  
Wireless Networks ◽  
Fading Channels ◽  
Mobile Networks ◽  
Spectrum Sharing ◽  
Performance Metrics ◽  
Relevant Literature ◽  
Future Generation ◽  
D2d Communications ◽  
Fifth Generation ◽  
Channel Parameters

In this chapter, the authors discuss various spectrum sharing techniques to enable device-to-device (D2D) communications over the licensed spectrum. First, they highlight the need of spectrum sharing in fifth-generation (5G) wireless and mobile networks. Then, they formulate the expressions of useful performance metrics e.g., outage probability, achievable sum-rate, and spectral efficiency of these schemes to refine physical layer design aspects. To give a better picture, they deduce some major practical scenarios where these techniques can play a crucial role in deploying future generation wireless networks. They also cover relevant literature on the spectrum sharing and D2D communications. Numerical and simulation results are provided to elucidate the effect of various system/channel parameters on the considered spectrum sharing schemes over Nakagami-m fading channels.

Machine Learning in 5G Multimedia Communications

2020 ◽  
pp. 361-382
Author(s):  
Dragorad A. Milovanovic ◽  
Zoran S. Bojkovic ◽  
Dragan D. Kukolj
Keyword(s):  
Machine Learning ◽  
Wireless Networks ◽  
Deep Learning ◽  
Complex Networks ◽  
Future Research ◽  
Fifth Generation ◽  
Data Formats ◽  
Research Challenges ◽  
Wireless Environments ◽  
Special Case

Machine learning (ML) has evolved to the point that this technique enhances communications and enables fifth-generation (5G) wireless networks. ML is great to get insights about complex networks that use large amounts of data, and for predictive and proactive adaptation to dynamic wireless environments. ML has become a crucial technology for mobile broadband communication. Special case goes to deep learning (DL) in immersive media. Through this chapter, the goal is to present open research challenges and applications of ML. An exploration of the potential of ML-based solution approaches in the context of 5G primary eMBB, mMTC, and uHSLLC services is presented, evaluating at the same time open issues for future research, including standardization activities of algorithms and data formats.

scholarly journals Physical Layer Security Requirements and Solutions for Device to Device Communication

2020 ◽  
Vol 9 (3) ◽  
pp. 3011-3013
Keyword(s):  
Physical Layer Security ◽  
Mobile Station ◽  
Physical Layer ◽  
Base Station ◽  
D2d Communication ◽  
Security Requirements ◽  
Security And Privacy ◽  
5G Network ◽  
Device To Device ◽  
Device To Device Communication

Device-to-Device (D2D) communication is used for cellular networks. D2d communication is the direct communication from one mobile station to other mobile station, without the involvement of the base station. By using the device to device communication lesser delay is possible. By using d2d communication along with 5G network improves the bit rate. 5G network provides the communication with more data rate and lesser delay. Security and privacy are very important for communication. In this paper security and privacy requirements of device to device communication and physical layer privacy solutions are discussed.

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