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5G Networks: Optimization, Machine Learning And Blockchain Technologies

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A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Electrical, Electronics and Communications Engineering".

Deadline for manuscript submissions: closed (31 December 2020) | Viewed by 28686

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Prof. Dr. Konstantinos E. Psannis

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Department of Applied Informatics, University of Macedonia, 54124 Thessaloniki, Greece
Interests: haptics; IoT; HCI
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Prof. Dr. Yutaka Ishibashi

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Graduate School of Engineering, Nagoya Institute of Technology, Nagoya 466-8555, Japan
Interests: QoS control; remote robot systems; haptic communications; networked virtual environments
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Prof. Dr. Dimitris E. Anagnostou

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Institute of Signals, Sensors & Systems, Heriot Watt University, Edinburgh, UK
Interests: 5G; lightweight and reconfigurable antennas; arrays; radar and RF sensing
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Prof. Dr. Sotirios K. Goudos

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Department of Physics, Aristotle University of Thessaloniki, 54124 Thessaloniki, Greece
Interests: antenna design; microwave components design; wireless communications; evolutionary algorithms; machine learning
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Dr. Panagiotis Sarigiannidis

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Department of Electrical and Computer Engineering, University of Western Macedonia, 50100 Kozani, Greece
Interests: IoT; 5G mobile communication; UAV; quality of service; radio access networks; computer network security; radio networks; artificial intelligence
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Prof. Dr. Jordi Mongay Batalla

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Faculty of Electronics and Informatics Technologies, Warsaw University of Technology, Warsaw, Poland
Interests: cybersecurity (risk assessment, security enforcement, vulnerabilities management); IP technologies (radio: 5G and 6G, core: network services chain, SDN, AI); applications (DLT and blockchain, Internet of Things, smart cities, multimedia) for the future internet
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Prof. Dr. Byung-Gyu Kim

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Department of IT Engineering, Sookmyung Women’s University, Seoul, Korea
Interests: visual sensor network; real-time object segmentation; deep learning for object detection; facial expression recognition
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Dr. Brij B Gupta

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National institute of Technology Kurukshetra, India
Interests: artificial intelligence; information security; cyber security; intrusion detection; cloud security, mobile security, web security, big data analytics; botnet detection; phishing; ddos attacks; network performance evaluation
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Prof. Dr. Shaohua Wan

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Shenzhen Institute for Advanced Study, University of Electronic Science and Technology of China, Shenzhen 518110, China
Interests: Internet of Things (IoT); edge computing; machine learning; computer vision; cyber physical systems; future Internet architecture and smart-energy
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Special Issue Information

Dear Colleagues,

The fifth generation (5G) of cellular communications is one of the key enabling technologies of the future and current information society. 5G network systems will serve users with data rates of several Gbps and will allow running new applications in mobile devices. In order to do so, several technical challenges have to be met. Machine learning (ML), will be a key feature of future 5G networks. Moreover, security for 5G network users is also an important issue due to the openness of some parts of the networks (slices for business), which is one of the main features and, at the same time, more critical for security. Management and network control require new technologies and solutions to ensure the operator’s manageability of the system.

Recent developments in blockchain technologies, including distributed ledger frameworks, consensus algorithms, smart contract engines, development libraries, and interfaces with other systems, are creating new types of services that were not possible before. Network design and management applications for these emerging technologies are a challenging task. We invite researchers to contribute original papers describing the design of 5G Networks as well as machine learning techniques and blockchain technologies.

Potential topics include but are not limited to the following:

Network planning for 5G networks;
Massive MIMO;
Optimization methods for 5G networks;
User association in5G networks;
Spectrum usage and allocation for 5G;
Green 5G networks;
Cognitive radio networks for 5G;
IoT and IoMM in 5G networks;
Multimedia-centric VR/AR service and technology in 5G networks;
Network planning for 5G;
Antenna design for 5G Networks;
Physical layer security in 5G Networks;
Blockchain backbone network and protocols for 5G networks;
Smart contracts for 5G networks;
Blockchain platforms and testbeds for 5G;
Security and privacy issues of blockchain technologies for 5G networks;
Blockchain applications for 5G networks;
Blockchain use case scenarios for 5G networks;
Blockchain business models in 5G;
Blockchain technology for 5G management and control.

Prof. Kostas E. Psannis
Prof. Yutaka Ishibashi
Prof. Dimitris E. Anagnostou
Prof. Sotirios K. Goudos
Prof. Panagiotis Sarigiannidis
Prof. Jordi Mongay Batalla
Prof. Byung-Gyu Kim
Prof. Brij B Gupta
Prof. Shaohua Wan
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Applied Sciences is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

network design
5G
IoT
machine learning
blockchain

Published Papers (8 papers)

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Research

26 pages, 4766 KiB

Open AccessArticle

Urban Free-Space Optical Network Optimization

by Revital Marbel, Boaz Ben-Moshe and Tal Grinshpoun

Appl. Sci. 2020, 10(21), 7872; https://doi.org/10.3390/app10217872 - 06 Nov 2020

Cited by 3 | Viewed by 1995

Abstract

This paper presents a set of graph optimization problems related to free-space optical communication networks. Such laser-based wireless networks require a line of sight to enable communication, thus a visibility graph model is used herein. The main objective is to provide connectivity from a communication source point to terminal points through the use of some subset of available intermediate points. To this end, we define a handful of problems that differ mainly in the costs applied to the nodes and/or edges of the graph. These problems should be optimized with respect to cost and performance. The problems at hand are shown to be NP-hard. A generic heuristic based on a genetic algorithm is proposed, followed by a set of simulation experiments that demonstrate the performance of the suggested heuristic method on real-life scenarios. The suggested genetic algorithm is compared with the Euclidean Steiner tree method. Our simulations show that in many settings, especially in dense graphs, the genetic algorithm finds lower-cost solutions than its competitor, while it falls behind in some settings. However, the run-time performance of the genetic algorithm is considerably better in graphs with 1000 nodes or more, being more than twice faster in some settings. We conclude that the suggested heuristic improves run-time performance on large-scale graphs and can handle a wider range of related optimization problems. The simulation results suggest that the 5G urban backbone may benefit significantly from using free-space optical networks. Full article

(This article belongs to the Special Issue 5G Networks: Optimization, Machine Learning And Blockchain Technologies)

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19 pages, 1115 KiB

Open AccessArticle

Hybrid NOMA/OMA-Based Dynamic Power Allocation Scheme Using Deep Reinforcement Learning in 5G Networks

by Hoang Thi Huong Giang, Tran Nhut Khai Hoan, Pham Duy Thanh and Insoo Koo

Appl. Sci. 2020, 10(12), 4236; https://doi.org/10.3390/app10124236 - 20 Jun 2020

Cited by 17 | Viewed by 3659

Abstract

Non-orthogonal multiple access (NOMA) is considered a potential technique in fifth-generation (5G). Nevertheless, it is relatively complex when applying NOMA to a massive access scenario. Thus, in this paper, a hybrid NOMA/OMA scheme is considered for uplink wireless transmission systems where multiple cognitive users (CUs) can simultaneously transmit their data to a cognitive base station (CBS). We adopt a user-pairing algorithm in which the CUs are grouped into multiple pairs, and each group is assigned to an orthogonal sub-channel such that each user in a pair applies NOMA to transmit data to the CBS without causing interference with other groups. Subsequently, the signal transmitted by the CUs of each NOMA group can be independently retrieved by using successive interference cancellation (SIC). The CUs are assumed to harvest solar energy to maintain operations. Moreover, joint power and bandwidth allocation is taken into account at the CBS to optimize energy and spectrum efficiency in order to obtain the maximum long-term data rate for the system. To this end, we propose a deep actor-critic reinforcement learning (DACRL) algorithm to respectively model the policy function and value function for the actor and critic of the agent (i.e., the CBS), in which the actor can learn about system dynamics by interacting with the environment. Meanwhile, the critic can evaluate the action taken such that the CBS can optimally assign power and bandwidth to the CUs when the training phase finishes. Numerical results validate the superior performance of the proposed scheme, compared with other conventional schemes. Full article

(This article belongs to the Special Issue 5G Networks: Optimization, Machine Learning And Blockchain Technologies)

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19 pages, 3796 KiB

Open AccessArticle

Two Optimization Algorithms for Name-Resolution Server Placement in Information-Centric Networking

by Jiaqi Li, Yiqiang Sheng and Haojiang Deng

Appl. Sci. 2020, 10(10), 3588; https://doi.org/10.3390/app10103588 - 22 May 2020

Cited by 4 | Viewed by 2201

Abstract

Information-centric networking (ICN) is an emerging network architecture that has the potential to address demands related to transmission latency and reliability in fifth-generation (5G) communication technology and the Internet of Things (IoT). As an essential component of ICN, name resolution provides the capability to translate identifiers into locators. Applications have different demands on name-resolution latency. To meet the demands, deploying name-resolution servers at the edge of the network by dividing it into multilayer overlay networks is effective. Moreover, optimization of the deployment of distributed name-resolution servers in such networks to minimize deployment costs is significant. In this paper, we first study the placement problem of the name-resolution server in ICN. Then, two algorithms called IIT-DOWN and IIT-UP are developed based on the heuristic ideas of inter-layer information transfer (IIT) and server reuse. They transfer server placement information and latency information between adjacent layers from different directions. Finally, experiments are conducted on both simulation networks and a real-world dataset. The experimental results reveal that the proposed algorithms outperform state-of-the-art algorithms such as the latency-aware hierarchical elastic area partitioning (LHP) algorithm in finding more cost-efficient solutions with a shorter execution time. Full article

(This article belongs to the Special Issue 5G Networks: Optimization, Machine Learning And Blockchain Technologies)

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19 pages, 2795 KiB

Open AccessArticle

Social-Aware-Based Resource Allocation for NOMA-Enhanced D2D Communications

by Wenying Gu and Qi Zhu

Appl. Sci. 2020, 10(7), 2446; https://doi.org/10.3390/app10072446 - 03 Apr 2020

Cited by 4 | Viewed by 2000

Abstract

In mobile communication systems, device-to-device (D2D) communication and nonorthogonal multiple access (NOMA) are effective ways to improve spectrum efficiency and system throughput. In the NOMA-based D2D system, social relationship among D2D users is introduced to form D2D clusters, and NOMA is used for many-to-one communication in each D2D cluster. This paper proposes a joint channel allocation and power control algorithm which decomposes the resource allocation problem into two subproblems: channel allocation and power control. Matching theory is utilized to allocate channels for D2D clusters and sequential convex programming is applied to transform the optimization target to a convex problem before solving it via genetic algorithm. Simulation results indicate the superiority of our algorithm in improving the system throughput on the basis of meeting users’ needs for files. Full article

(This article belongs to the Special Issue 5G Networks: Optimization, Machine Learning And Blockchain Technologies)

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17 pages, 548 KiB

Open AccessArticle

Performance Analysis of D2D Communication with Retransmission Mechanism in Cellular Networks

by Jianfang Xin, Qi Zhu, Guangjun Liang and Tianjiao Zhang

Appl. Sci. 2020, 10(3), 1097; https://doi.org/10.3390/app10031097 - 06 Feb 2020

Cited by 6 | Viewed by 3361

Abstract

In this paper, we focus on the performance analysis of device-to-device (D2D) underlay communication in cellular networks. First, we develop a spatiotemporal traffic model to model a retransmission mechanism for D2D underlay communication. The D2D users in backlogged statuses are modeled as a thinned Poisson point process (PPP). To capture the characteristics of sporadic wireless data generation and limited buffer, we adopt queuing theory to analyze the performance of dynamic traffic. Furthermore, a feedback queuing model is adopted to analyze the performance with retransmission strategy. With the consideration of interference and channel fading, the service probability of the queue departure process is determined by the received signal-to-interference-plus-noise ratio (SINR). Then, the embedded Markov chain is employed to depict the queuing status in the D2D user buffer. We compute its steady-state distribution and derive the closed-form expressions of performance metrics, namely the average queue length, average throughput, average delay, and dropping probability. Simulation results show the validity and rationality of the theoretical analysis with different channel parameters and D2D densities. In addition, the simulation explores the dropping probability of a D2D user with and without the retransmission strategy for different D2D links in the system. When the arrival rate is comparatively high, the optimal throughput is reached after fewer retransmission attempts as a result of the limited buffer. Full article

(This article belongs to the Special Issue 5G Networks: Optimization, Machine Learning And Blockchain Technologies)

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20 pages, 615 KiB

Open AccessArticle

Multi-Objective Optimization of Massive MIMO 5G Wireless Networks towards Power Consumption, Uplink and Downlink Exposure

by Michel Matalatala, Margot Deruyck, Sergei Shikhantsov, Emmeric Tanghe, David Plets, Sotirios Goudos, Kostas E. Psannis, Luc Martens and Wout Joseph

Appl. Sci. 2019, 9(22), 4974; https://doi.org/10.3390/app9224974 - 19 Nov 2019

Cited by 20 | Viewed by 5166

Abstract

The rapid development of the number of wireless broadband devices requires that the induced uplink exposure be addressed during the design of the future wireless networks, in addition to the downlink exposure due to the transmission of the base stations. In this paper, the positions and power levels of massive MIMO-LTE (Multiple Input Multiple Output-Long Term Evolution) base stations are optimized towards low power consumption, low downlink and uplink electromagnetic exposure and maximal user coverage. A suburban area in Ghent, Belgium has been considered. The results show that the higher the number of BS antenna elements, the fewer number of BSs the massive MIMO network requires. This leads to a decrease of the downlink exposure (−12% for the electric field and −32% for the downlink dose) and an increase of the uplink exposure (+70% for the uplink dose), whereas both downlink and uplink exposure increase with the number of simultaneous served users (+174% for the electric field and +22% for the uplink SAR). The optimal massive MIMO network presenting the better trade-off between the power consumption, the total dose and the user coverage has been obtained with 37 64-antenna BSs. Moreover, the level of the downlink electromagnetic exposure (electric field) of the massive MIMO network is 5 times lower than the 4G reference scenario. Full article

(This article belongs to the Special Issue 5G Networks: Optimization, Machine Learning And Blockchain Technologies)

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11 pages, 6405 KiB

Open AccessArticle

A 28 GHz 5G Phased Array Antenna with Air-Hole Slots for Beam Width Enhancement

by Hojoo Lee, Sungpeel Kim and Jaehoon Choi

Appl. Sci. 2019, 9(20), 4204; https://doi.org/10.3390/app9204204 - 09 Oct 2019

Cited by 15 | Viewed by 6144

Abstract

In this paper, a 28 GHz fifth-generation (5G) phased array antenna with air-hole slots for beam width enhancement is proposed. The proposed antenna consists of eight dipole radiators on a mobile handset-sized ground with air-hole slots between the two adjacent elements for enhancing the half power beam width (HPBW) in the elevation plane. The dimensions of the proposed antenna are 130 mm × 42 mm × 0.127 mm. The proposed array antenna satisfies a −10 dB reflection coefficient in the frequency range from 27.2 to 29.2 GHz with a peak gain of 10.33 dBi and a side lobe level (SLL) of 13 dB. In addition to its good performance, the proposed antenna has a very wide HPBW (measured) in the elevation plane, up to 219 degree with a scan coverage of ±45 degree in the azimuth plane. The proposed antenna demonstrates excellent hemispheric beam coverage for 5G mobile handset devices and can enable cost-effective mass production. Full article

(This article belongs to the Special Issue 5G Networks: Optimization, Machine Learning And Blockchain Technologies)

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13 pages, 1179 KiB

Open AccessArticle

Application Research of Multi-Mode Relay in Future Heterogeneous Networks

by Wenle Bai, Yu Xiao, Danping Hu and Yongmei Zhang

Appl. Sci. 2019, 9(18), 3934; https://doi.org/10.3390/app9183934 - 19 Sep 2019

Cited by 1 | Viewed by 2242

Abstract

The fast increase of users in existing mobile networks requires more base stations (BSs) to bear more communication traffic. Future heterogeneous network is considered to be a promising candidate architecture to meet the demands of wireless networks under scarcity of radio frequency (RF) resources. In this paper, we present a multi-mode relay (MMR) model based on two-way relay technology, which is applied to heterogeneous hierarchical wireless networks (HHWN), and set up a system model of HHWN with 3 tiers, 2 users between the macrocell, and the picocell as the multi-mode relay (MMR). Specifically, we consider the new system with unequal relay emission power situation, which is usually researched in the traditional literature with equal relay emission powers. Based on this idea, we define the two-way SINR ratio, derive the mathematical formulas of outage error probability with channel estimation errors, and verify theoretical expressions by data simulations. For further comparison, several experiments are implemented to illuminate the effect on outage probability among different levels of relay emission power, noise power, and signal power. Furthermore, several conclusions are obtained, which have some meanings for implementing MMR in future heterogeneous networks. Full article

(This article belongs to the Special Issue 5G Networks: Optimization, Machine Learning And Blockchain Technologies)

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