[go: up one dir, main page]
 
 
Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
5.3
2.5
Journals
Applied Sciences
Special Issues
5G Networks: Optimization, Machine Learning And Blockchain Technologies
applsci-logo
Submit to Applied Sciences Review for Applied Sciences Propose a Special Issue

Journal Menu

► Journal Menu

Journal Browser

► Journal Browser
share announcement

5G Networks: Optimization, Machine Learning And Blockchain Technologies

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 35029

Special Issue Editors

Prof. Dr. Konstantinos E. Psannis

E-Mail Website
Guest Editor
Department of Applied Informatics, University of Macedonia, 54124 Thessaloniki, Greece
Interests: Internet of Things (IoT); artificial intelligence (AI); haptic communications; QoS control; big data analysis; cloud computing; digital twins
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Yutaka Ishibashi

E-Mail Website
Guest Editor
Department of Business Management, Faculty of Business Administration, Aichi Sangyo University, Okazaki 444-0005, Japan
Interests: QoS control; remote robot systems; haptic communications; networked virtual environments
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Dimitris E. Anagnostou

E-Mail Website
Guest Editor
Institute of Signals, Sensors & Systems, Heriot Watt University, Edinburgh, UK
Interests: 5G; lightweight and reconfigurable antennas; arrays; radar and RF sensing
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Sotirios K. Goudos

E-Mail Website
Guest Editor
ELEDIA@AUTH, ELEDIA Research Center, Department of Physics, Aristotle University of Thessaloniki (AUTH), 54124 Thessaloniki, Greece
Interests: artificial intelligence and machine learning; evolutionary algorithms; wireless communications; antennas; microwaves
Special Issues, Collections and Topics in MDPI journals
Dr. Panagiotis Sarigiannidis

E-Mail Website
Guest Editor
Department of Electrical and Computer Engineering, University of Western Macedonia (UOWM), 50150 Kozani, Greece
Interests: telecommunication networks; simulation programming; Internet of Things; cybersecurity
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Jordi Mongay Batalla

E-Mail Website
Guest Editor
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
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Byung-Gyu Kim

E-Mail Website
Guest Editor
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
Special Issues, Collections and Topics in MDPI journals
Dr. Brij B Gupta

E-Mail Website
Guest Editor
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
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Shaohua Wan

E-Mail Website
Guest Editor
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
Special Issues, Collections and Topics in MDPI journals

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

Benefits of Publishing in a Special Issue

  • Ease of navigation: Grouping papers by topic helps scholars navigate broad scope journals more efficiently.
  • Greater discoverability: Special Issues support the reach and impact of scientific research. Articles in Special Issues are more discoverable and cited more frequently.
  • Expansion of research network: Special Issues facilitate connections among authors, fostering scientific collaborations.
  • External promotion: Articles in Special Issues are often promoted through the journal's social media, increasing their visibility.
  • e-Book format: Special Issues with more than 10 articles can be published as dedicated e-books, ensuring wide and rapid dissemination.

Further information on MDPI's Special Issue policies can be found here.

Published Papers (8 papers)

Download All Papers
Order results
Result details
Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:

Research

26 pages, 4766 KiB  
Article
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 - 6 Nov 2020
Cited by 3 | Viewed by 2588
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 [...] Read more.
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)
►▼ Show Figures

Figure 1

Figure 1
<p>Visibility graph simulation.</p> Full article ">Figure 2
<p>An example of two different spanning trees of <math display="inline"> <semantics> <mrow> <mi>K</mi> <mn>5</mn> </mrow> </semantics> </math> (full graph with 5 nodes). Graph A is <math display="inline"> <semantics> <mrow> <mi>K</mi> <mn>5</mn> </mrow> </semantics> </math>, Graph B has only two leaves, and Graph C has the maximum leaf number (four leaves).</p> Full article ">Figure 3
<p>A simple visibility graph that displays a terrain with a set of (green and gray) buildings: (<b>a</b>) an example of buildings in a “real” city map; (<b>b</b>) an enlarged part of the map, where the red lines represent FSO links between two buildings, and the gray dashed lines are non-FSO links; and (<b>c</b>) the red-–blue visibility graph of the terrain, where each point in the graph corresponds to a building.</p> Full article ">Figure 4
<p>The visibility and optimal graphs of the example in <a href="#applsci-10-07872-f003" class="html-fig">Figure 3</a>: (<b>a</b>) the visibility graph, (<b>b</b>) an optimal solution, and (<b>c</b>) another optimal solution.</p> Full article ">Figure 5
<p>An example of a red–blue edge-weighted graph. The optimal solution (top blue) uses four edges, which leads to an overall edge cost of 4; terminal connectivity is achievable with just three edges (bottom green), but with a higher edge cost of 5.</p> Full article ">Figure 6
<p>Visibility graph representation on a simplified terrain. Left: a graph. Mid: the vertices are located on a polygon of <span class="html-italic">n</span> edges; the black polygon represents a “wall”. Each edge <math display="inline"> <semantics> <mrow> <mi>e</mi> <mo>(</mo> <mi>u</mi> <mo>,</mo> <mi>v</mi> <mo>)</mo> </mrow> </semantics> </math> in the graph is marked as a (blue) segment in the polygon. Right: each visibility segment is marked in white to allow a “visibility window” related to each edge in the graph.</p> Full article ">Figure 7
<p>Basic flow of the genetic algorithm (GA).</p> Full article ">Figure 8
<p>Example of valid random solutions. The left side of the figure shows an example red–blue visibility graph, whereas two different valid solutions are depicted to its right.</p> Full article ">Figure 9
<p>Illustration of two possible paths for connecting the blue vertex <span class="html-italic">v</span> to the component of the sink vertex <span class="html-italic">s</span>, one through relay vertex <math display="inline"> <semantics> <msub> <mi>v</mi> <mn>1</mn> </msub> </semantics> </math>, and another through relay vertex <math display="inline"> <semantics> <msub> <mi>v</mi> <mn>2</mn> </msub> </semantics> </math>.</p> Full article ">Figure 10
<p>The iterative flow of <span class="html-italic">nextGeneration</span>.</p> Full article ">Figure 11
<p>Example of an invalid crossover. The right graph is the result of a crossover between the two left graphs without applying <math display="inline"> <semantics> <mrow> <mi>r</mi> <mi>a</mi> <mi>n</mi> <mi>d</mi> <mi>o</mi> <mi>m</mi> <mi>C</mi> <mi>o</mi> <mi>n</mi> <mi>n</mi> <mi>e</mi> <mi>c</mi> <mi>t</mi> </mrow> </semantics> </math>.</p> Full article ">Figure 12
<p>A 3D map of Manhattan Island, as defined in Open Street Map (OSM). Each building is stored as a 3D shape, allowing us to compute the associated visibility graph.</p> Full article ">Figure 13
<p>Three examples of random red–blue visibility graphs. Graph (<b>a</b>) has 100 nodes and <math display="inline"> <semantics> <mrow> <mn>0.3</mn> </mrow> </semantics> </math> edge density. Graph (<b>b</b>) has 50 nodes and <math display="inline"> <semantics> <mrow> <mn>0.5</mn> </mrow> </semantics> </math> edge density. Graph (<b>c</b>) has only 20 nodes, which is useful for quickly testing the algorithms.</p> Full article ">Figure 14
<p>Manhattan Island visibility graph: (<b>a</b>) a map of Manhattan Island in New York, (<b>b</b>) a chart of all the buildings in that map (using OSM); and (<b>c</b>) the building visibility graph.</p> Full article ">Figure 15
<p>Two methods comparison example. The full red–blue visibility graph (<b>top</b>) has a total cost of 60. The resulting graphs of the 2-factor (<b>bottom left</b>) method and the GA method (<b>bottom right</b>) have total costs of 15 and 12, respectively.</p> Full article ">Figure 16
<p>Run time comparison on graphs with 200 vertices at different densities. This graph presents the run time of the 2-factor approximation method (<b>green</b>) and the GA (<b>blue</b>).</p> Full article ">Figure 17
<p>Run time comparison on graphs with 2000 vertices at different densities. This graph presents the run time of the 2-factor approximation method (<b>green</b>) and the GA (<b>blue</b>).</p> Full article ">Figure 18
<p>Cost difference comparison on graphs with 200 vertices at different densities. This graph presents the average cost difference between the GA’s solutions and those of the 2-factor approximation method, which are marked by the fixed black line. The blue region represents graph densities in which the 2-factor approximation method finds better solutions.</p> Full article ">Figure 19
<p>Cost difference comparison on graphs with 2000 vertices at different densities. This graph presents the average cost difference between the solutions of the GA and those of the 2-factor approximation method, which are marked by the fixed black line. The blue region represents graph densities in which the 2-factor approximation method finds better solutions.</p> Full article ">Figure 20
<p>Run time comparison on graphs with different sizes and 0.15 density. This graph presents the run time of the 2-factor approximation method (<b>green</b>) and the GA (<b>blue</b>).</p> Full article ">Figure 21
<p>Cost difference comparison on graphs with different sizes and 0.15 density. This graph presents the average cost difference between the solutions of the GA and those of the 2-factor approximation method, which are marked by the fixed black line. The blue region represents graph sizes in which the 2-factor approximation method finds better solutions.</p> Full article ">Figure 22
<p>Run time of the GA on graphs with 4000 vertices at different densities.</p> Full article ">Figure 23
<p>Run time of the GA on graphs with 8000 vertices at different densities.</p> Full article ">Figure 24
<p>Run time of the GA on graphs with 16,000 vertices at different densities.</p> Full article ">Figure 25
<p>The cost reduction of the GA over 60 generations on an example problem. The fixed red line represents the result of the 2-factor approximation method.</p> Full article ">Figure 26
<p>The cost reduction of the GA over 1000 generations on an example problem. The fixed red line represents the result of the 2-factor approximation method.</p> Full article ">Figure 27
<p>Cost differences histogram. This histogram demonstrates the cost differences between the GA’s solutions and those of the 2-factor approximation method, which are denoted by the red line. Positive values represent a better (lower) solution cost of the GA.</p> Full article ">Figure 28
<p>Time differences histogram. This histogram demonstrates the differences in run time between the GA and the 2-factor approximation method, which are denoted by the red line. Positive values represent better (faster) run time of the GA.</p> Full article ">Figure 29
<p>Multi-source graph example. Graph (<b>a</b>) is a full red–blue visibility graph comprised of two subgraphs with a single connecting edge (with weight 0). Graph (<b>b</b>) is the GA result for graph (a). Note that because we artificially connected two subgraphs with a single edge of weight 0, we can run the algorithm on each subgraph separately and achieve the same result.</p> Full article ">
19 pages, 1115 KiB  
Article
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 20 | Viewed by 4754
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 [...] Read more.
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)
►▼ Show Figures

Figure 1

Figure 1
<p>System model of the proposed scheme.</p> Full article ">Figure 2
<p>Time frame of the cognitive users’ operations.</p> Full article ">Figure 3
<p>Markov chain model of the primary channel.</p> Full article ">Figure 4
<p>The agent–environment interaction process.</p> Full article ">Figure 5
<p>The structure of deep actor-critic reinforcement learning.</p> Full article ">Figure 6
<p>The deep neural network in the critic.</p> Full article ">Figure 7
<p>The deep neural network in the actor.</p> Full article ">Figure 8
<p>The convergence rate of the proposed actor-critic deep reinforcement learning with different training steps in each episode.</p> Full article ">Figure 9
<p>The convergence rate of the proposed actor-critic deep reinforcement learning according to different learning rate values.</p> Full article ">Figure 10
<p>Average transmission rate according to different values for mean harvested energy.</p> Full article ">Figure 11
<p>Energy efficiency according to different values of harvested mean energy.</p> Full article ">Figure 12
<p>Average transmission rate according to noise variance.</p> Full article ">Figure 13
<p>Energy efficiency according to noise variance.</p> Full article ">
19 pages, 3796 KiB  
Article
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 2792
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 [...] Read more.
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)
►▼ Show Figures

Figure 1

Figure 1
<p>The nested structure of deterministic latency name resolution (DLNR). The physical network is partitioned into several hierarchical elastic areas (HEAs). These HEAs are nested organized, and each HEA has an HEA Manager (HM) to provide a name-resolution service. Each layer is constrained by an upper bound of latency.</p> Full article ">Figure 2
<p>An example operation in inter-layer information transfer (IIT)-DOWN. Nodes do not change in different layers, but the connections among them may be different. Nodes in black are chosen for the placement of name-resolution servers. Nodes in white represent those that have not been partitioned. The HMs chosen from level <math display="inline"><semantics> <mi>i</mi> </semantics></math> are removed in level <math display="inline"><semantics> <mrow> <mi>i</mi> <mo>−</mo> <mn>1</mn> </mrow> </semantics></math>, and grey nodes represent nodes that satisfy the latency constraint from these HMs. There are four HMs in total in this example.</p> Full article ">Figure 3
<p>An example operation in IIT-UP. The network of each layer is solved in order from low to high. The HMs in the level <math display="inline"><semantics> <mi>i</mi> </semantics></math> layer constitute the topology of the level <math display="inline"><semantics> <mrow> <mi>i</mi> <mo>+</mo> <mn>1</mn> </mrow> </semantics></math> layer.</p> Full article ">Figure 4
<p>The network scenario of simulation experiments.</p> Full article ">Figure 5
<p>Deployment cost comparison for each algorithm with different network sizes.</p> Full article ">Figure 6
<p>HM count at each layer. In each layer, inter-layer information transfer (IIT) algorithms perform better than other algorithms. IIT-DOWN and multilayer minimum dominating set (MDSM) algorithms need less name-resolution servers in higher-level layers. (<b>a</b>) The layer with the latency upper bound at 50 ms. Note that the results of IIT-DOWN and MDSM overlap. (<b>b</b>) The layer with the latency upper bound at 25 ms. (<b>c</b>) The layer with the latency upper bound at 10 ms.</p> Full article ">Figure 7
<p>Execution time comparison of each algorithm with different network sizes. IIT algorithms require less time to solve a problem, which means that they can solve problems in more extensive networks. The latency-aware hierarchical elastic area partitioning (LHP) algorithms may not complete the calculation in a reasonable time. (<b>a</b>) Comparison of all five algorithms. (<b>b</b>) Comparisons of MDSM, IIT-DOWN, and IIT-UP are presented in detail.</p> Full article ">Figure 8
<p>Average name-resolution latency at each layer. LHP provides a shorter average latency at the lowest level because it uses more name-resolution nodes. However, in higher-level layers, IIT algorithms perform better. (<b>a</b>) The layer with the latency upper bound at 50 ms. Note that the results of IIT-DOWN and MDSM overlap. (<b>b</b>) The layer with the latency upper bound at 25 ms. (<b>c</b>) The layer with the latency upper bound at 10 ms.</p> Full article ">Figure 9
<p>Impact of <math display="inline"><semantics> <mi>k</mi> </semantics></math> on deployment costs.</p> Full article ">Figure 10
<p>The coverage rate of K placement algorithms. (<b>a</b>) The layer with the latency upper bound at 50 ms. (<b>b</b>) The layer with the latency upper bound at 25 ms. (<b>c</b>) The layer with the latency upper bound at 10 ms.</p> Full article ">
19 pages, 2795 KiB  
Article
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 - 3 Apr 2020
Cited by 6 | Viewed by 2443
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 [...] Read more.
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)
►▼ Show Figures

Figure 1

Figure 1
<p>System model (physical domain).</p> Full article ">Figure 2
<p>System model (social domain).</p> Full article ">Figure 3
<p>System model of Proposition 1.</p> Full article ">Figure 4
<p>Average swap operations for different numbers of cellular channels with different D2D content requesters.</p> Full article ">Figure 5
<p>Accessed probability for different numbers of cellular channels with different numbers of D2D content providers, <math display="inline"><semantics> <mrow> <mi>N</mi> <mo>=</mo> <mn>5</mn> </mrow> </semantics></math>.</p> Full article ">Figure 6
<p>Accessed probability for different numbers of cellular channels with different numbers of D2D content requesters, <math display="inline"><semantics> <mrow> <mi>K</mi> <mo>=</mo> <mn>200</mn> </mrow> </semantics></math>.</p> Full article ">Figure 7
<p>Sum rate of D2D clusters for different numbers of cellular channels with different numbers of D2D content providers, <math display="inline"><semantics> <mrow> <mi>N</mi> <mo>=</mo> <mn>5</mn> </mrow> </semantics></math>.</p> Full article ">Figure 8
<p>Sum rate of D2D clusters for different numbers of cellular channels with different numbers of D2D content requesters, <math display="inline"><semantics> <mrow> <mi>K</mi> <mo>=</mo> <mn>200</mn> </mrow> </semantics></math>.</p> Full article ">
17 pages, 548 KiB  
Article
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 - 6 Feb 2020
Cited by 8 | Viewed by 4056
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 [...] Read more.
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)
►▼ Show Figures

Figure 1

Figure 1
<p>A hybrid network consisting of both cellular and D2D links.</p> Full article ">Figure 2
<p>Feedback queuing model.</p> Full article ">Figure 3
<p>The D2D performance parameters versus packet arrival rate under different path loss factors <math display="inline"><semantics> <mi>α</mi> </semantics></math>.</p> Full article ">Figure 4
<p>The D2D performance parameters versus packet arrival rate for different D2D densities <math display="inline"><semantics> <msub> <mi>λ</mi> <mi>D</mi> </msub> </semantics></math>.</p> Full article ">Figure 5
<p>The dropping probability for D2D communication under different retransmission times <span class="html-italic">K</span>.</p> Full article ">Figure 6
<p>The average throughput versus the number of retransmission times under different mean arrival rates <span class="html-italic">q</span>.</p> Full article ">
20 pages, 615 KiB  
Article
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 26 | Viewed by 6092
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, [...] Read more.
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)
►▼ Show Figures

Figure 1

Figure 1
<p>Multi-cell massive Multiple Input Multiple Output (MIMO) network system model.</p> Full article ">Figure 2
<p>Selected area in Ghent, Belgium and the possible location of the base stations.</p> Full article ">Figure 3
<p>Optimization algorithm implemented in the capacity-based network deployment, designing optimized networks towards power consumption, downlink (DL) and uplink (UL) exposure.</p> Full article ">Figure 4
<p>Number of simultaneously served users on a hourly basis in Ghent, Belgium.</p> Full article ">Figure 5
<p>Impact of the number of users on the DL and UL exposure (scenario 1, tri-objective optimization).</p> Full article ">Figure 6
<p>Impact of the number of users on the DL and UL doses (scenario 1, bi-objective optimization).</p> Full article ">Figure 7
<p>Impact of the number of users on the number of base stations (BSs) deployed.</p> Full article ">Figure 8
<p>Impact of the number of antenna elements on the DL and UL exposure (scenario 2, tri-objective optimization).</p> Full article ">Figure 9
<p>Impact of the number of antenna elements on the DL and UL exposure (scenario 2, bi-objective optimization).</p> Full article ">Figure 10
<p>Pareto front for the bi-objective optimization (224 users and various BS antenna elements: 16, 32, 64, 128).</p> Full article ">Figure 11
<p>Comparision of the cumulative distribution function (CDF) of the downlink exposure (scenario 2): 4G vs. 5G (<math display="inline"><semantics> <mrow> <mi>E</mi> <mn>4</mn> <mi>G</mi> </mrow> </semantics></math> is the DL electric field due to a 4G BS, while <math display="inline"><semantics> <mrow> <mi>E</mi> <mn>5</mn> <mi>G</mi> </mrow> </semantics></math> refers to the DL electric field due to a 5G BS).</p> Full article ">
11 pages, 6405 KiB  
Article
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 - 9 Oct 2019
Cited by 16 | Viewed by 7107
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 [...] Read more.
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)
►▼ Show Figures

Figure 1

Figure 1
<p>The azimuth and elevation plane versus the radiation pattern: (<b>a</b>) half power beam width (HPBW) in the elevation plane and shaded area; (<b>b</b>) hemispheric beam coverage required by mobile handsets.</p> Full article ">Figure 2
<p>Structure of the proposed two-element array antenna: (<b>a</b>) top view (<b>b</b>) cross-sectional view.</p> Full article ">Figure 3
<p>Simulated reflection coefficient and isolation level of the proposed two-element array.</p> Full article ">Figure 4
<p>Implementation of the slot in the ground: (<b>a</b>) visualized mechanism of the slot as a complementary dipole source (<b>b</b>) multiplication effect of the complementary dipole source on the radiation pattern of the two-element array.</p> Full article ">Figure 5
<p>Realized gain for various values of the slot width (W).</p> Full article ">Figure 6
<p>Introduction of the air-hole superposed with the slot in the ground: (<b>a</b>) top view of the simulation environment for surface current intensity around the slot (<b>b</b>) comparison of surface current intensity from the supposed two cases.</p> Full article ">Figure 7
<p>Comparison of HPBW at 28 GHz in the YZ plane for the supposed three cases.</p> Full article ">Figure 8
<p>Top view of the proposed 1 × 8 phased array antenna on a mobile handset ground.</p> Full article ">Figure 9
<p>Reflection coefficient of the proposed antenna and isolation between the elements.</p> Full article ">Figure 10
<p>Radiation pattern of the proposed phased array antenna at 28 GHz when the phase difference between element ports is 0 degree (0 degree scan): (<b>a</b>) XY plane (<b>b</b>) YZ plane.</p> Full article ">Figure 11
<p>Radiation pattern of the proposed phased array antenna at 28 GHz in the XY plane (azimuth) when the phase difference between element ports is varied from 0 degree to 120 degree with a step of 60 degree.</p> Full article ">Figure 12
<p>Fabricated 1 × 8 array antenna: (<b>a</b>) top view (<b>b</b>) bottom view</p> Full article ">Figure 13
<p>Measured and simulated reflection coefficient of the proposed array antenna.</p> Full article ">Figure 14
<p>Measured and simulated radiation patterns of the proposed array antenna at 28 GHz: (<b>a</b>) XY plane with a 0 degree phase difference; (<b>b</b>) YZ plane with a 0 degree phase difference; (<b>c</b>) XY plane with a 120 degree phase difference.</p> Full article ">
13 pages, 1179 KiB  
Article
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 2674
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) [...] Read more.
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)
►▼ Show Figures

Figure 1

Figure 1
<p>Multi-mode relay (MMR) running in the modes of picocell BS and femtocell BS.</p> Full article ">Figure 2
<p>3-tier HHWN max SINR downlink coverage regions with macrocells, picocells, and femtocells.</p> Full article ">Figure 3
<p>System model of multi-mode relay <math display="inline"><semantics> <mrow> <msub> <mi>p</mi> <mrow> <mi>r</mi> <mn>1</mn> </mrow> </msub> <mo>≠</mo> <msub> <mi>p</mi> <mrow> <mi>r</mi> <mn>2</mn> </mrow> </msub> </mrow> </semantics></math>.</p> Full article ">Figure 4
<p>System simulation diagram.</p> Full article ">Figure 5
<p>Hierarchical heterogeneous network model (7 macro cells, 70 micro cells, 140 users).</p> Full article ">Figure 6
<p>Outage probability of HHWN with MMR of different emission powers.</p> Full article ">Figure 7
<p>Outage probability of HHWN with MMR as different relay power <math display="inline"><semantics> <msub> <mi>p</mi> <mrow> <mi>r</mi> <mn>2</mn> </mrow> </msub> </semantics></math> equals 9/10,1/2,1/3,1/4,1/5 times <math display="inline"><semantics> <msub> <mi>p</mi> <mrow> <mi>r</mi> <mn>1</mn> </mrow> </msub> </semantics></math>.</p> Full article ">Figure 8
<p>Spectral efficiency of HHWN with MMR as different relay power <math display="inline"><semantics> <msub> <mi>p</mi> <mrow> <mi>r</mi> <mn>2</mn> </mrow> </msub> </semantics></math> equals 9/10,1/2,1/3,1/4,1/5 times <math display="inline"><semantics> <msub> <mi>p</mi> <mrow> <mi>r</mi> <mn>1</mn> </mrow> </msub> </semantics></math>.</p> Full article ">Figure 9
<p>Outage probability of HHWN with MMR as different noise power <math display="inline"><semantics> <mrow> <msub> <mi>p</mi> <mi>n</mi> </msub> <mo>=</mo> <msubsup> <mi>σ</mi> <mi>E</mi> <mn>2</mn> </msubsup> <mo>=</mo> <mn>10</mn> <mspace width="0.166667em"/> <mi>dBm</mi> <mo>,</mo> <mn>0</mn> <mspace width="0.166667em"/> <mi>dBm</mi> <mo>,</mo> <mo>−</mo> <mn>10</mn> <mspace width="0.166667em"/> <mi>dBm</mi> <mo>,</mo> <mo>−</mo> <mn>20</mn> <mspace width="0.166667em"/> <mi>dBm</mi> <mo>,</mo> <mo>−</mo> <mn>30</mn> <mspace width="0.166667em"/> <mi>dBm</mi> </mrow> </semantics></math>.</p> Full article ">
Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:
 
Displaying articles 1-8
Back to TopTop