sensors-logo

Journal Browser

Journal Browser

Special Issue "Emerging Technologies in Communications and Networking: 5G and Beyond"

A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Communications".

Deadline for manuscript submissions: closed (15 December 2020).

Special Issue Editors

Dr. Adrian Kliks
Website
Guest Editor
Poznan University of Technology, Poland
Interests: wireless communications; spectrum management; waveform design
Special Issues and Collections in MDPI journals
Dr. Paweł Sroka
Website
Guest Editor
Poznan University of Technology, Poland
Interests: telecommunications; wireless systems
Special Issues and Collections in MDPI journals
Dr. Cynthia Hood
Website
Guest Editor
Illinois Institute of Technology, Chicago, IL, USA
Dr. Nikos Dimitrou
Website
Guest Editor
Networks Laboratory, Institute of Informatics and Telecommunications, National Centre for Scientific Research "Demokritos", Greece
Interests: wireless communications
Special Issues and Collections in MDPI journals

Special Issue Information

The recent trends in the development of communication systems and networking, both wired and wireless, show the continuously increasing impact of softwarization and effective algorithmic design on the resultant systems’ performance. This process has already had a strong impact on 5G-family standards. However, the migration from hardware and application oriented solutions, typical of older generations of communication systems, towards highly virtualized schemes was only possible thanks to the practical implementation of recent technologies such as software defined networks (SDN) with network function virtualization (NFV), software defined radio (SDR), and cognitive radio (CR). The overall softwarization of communication networks has provided various benefits for network operators and changed the industry portfolio related to communication networks. The application of new technological solutions does not necessarily indicate changes to hardware configuration as it may be completed by means of the software as well.

Contemporary networks are so complex that proper and optimized design of the considered algorithms for wireless and wired communications is a must. It is envisaged that future communication systems will have to face unprecedented challenges related to the high degree of diversity of prospective applications, types of devices, system requirements, types of environment, etc. Moreover, the foreseen increase in the number of active devices within the network, as well as in the volume of acquired and processed data, paves the way for the implementation of new paradigms in the future. Finally, the scale of the optimization problems, the presence of data with different levels of reliability and veracity, the various types and sizes of data, and the various processing delay factors indicate the need for new algorithmic designs for future wireless networks.

This Special Issue covers topics related to new emerging technologies in communications and networking, focusing on 5G networks and beyond. We invite the authors to submit new research and review papers considering (but not limited to) the topics below:

  • Solutions in SDN, VNF, CR and SDR;
  • Machine learning and application of artificial intelligence for 5G and beyond;
  • Big data processing for wireless communications and networking;
  • Fuzzy logic schemes for wireless communications;
  • Quantum communications and computing;
  • Novel algorithms for radio access networks, OpenRAN;
  • Algorithms for massive communication (i.e., fmassive machine-to-machine communications and massive MIMO schemes);
  • Algorithms for high mobility scenarios including V2X and U2X scenarios;
  • Distributed, centralized, and hybrid architecture design for future wireless communications and networking;
  • Advanced signal processing algorithms;
  • Self-organization algorithms for wireless networks;
  • Advanced radio resource management for 5G and beyond;
  • Cooperation algorithms for wireless systems;
  • Game theory applications for wireless communications;
  • Distributed computing (in edge, fog, and cloud) and processing.

Dr. Adrian Kliks
Dr. Paweł Sroka
Dr. Cynthia Hood
Dr. Nikos Dimitrou
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 papers will be 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. Sensors 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 2200 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

  • algorithms
  • wireless and wired communications and networking
  • 5G systems and beyond
  • bid data processing
  • wireless system virtualization and softwarization
  • massive computing

Published Papers (8 papers)

Order results
Result details
Select all
Export citation of selected articles as:

Research

Jump to: Other

Open AccessArticle
The Optical Signal-to-Crosstalk Ratio for the MBA(N, e, g) Switching Fabric
Sensors 2021, 21(4), 1534; https://doi.org/10.3390/s21041534 - 23 Feb 2021
Viewed by 173
Abstract
The banyan-type switching networks, well known in switching theory and called the logdN switching fabrics, are composed of symmetrical switching elements of size d×d. In turn, the modified baseline architecture, called the MBA(N,e [...] Read more.
The banyan-type switching networks, well known in switching theory and called the logdN switching fabrics, are composed of symmetrical switching elements of size d×d. In turn, the modified baseline architecture, called the MBA(N,e,g), is only partially built from symmetrical optical switching elements, and it is constructed mostly from asymmetrical optical switching elements. Recently, it was shown that the MBA(N,e,g) structure requires a lower number of passive as well as active optical elements than the banyan-type switching fabric of the same capacity and functionality, which makes it an attractive solution. However, the optical signal-to-crosstalk ratio for the MBA(N,e,g) was not investigated before. Therefore, in this paper, the optical signal-to-crosstalk ratio in the MBA(N,e,g) was determined. Such crosstalk influences the output signal’s quality. Thus, if such crosstalk is lower, the signal quality is better. The switching fabric proposed in the author’s previous work has lower optical signal losses than a typical Beneš and banyan-type switching networks of this same capacity and functionality, which gives better quality of transmitted optical signals at the switching node’s output. The investigated MBA(N,e,g) architecture also contains one stage fewer than banyan-type network of the same capacity, which is an essential feature from the optical switching point of view. Full article
(This article belongs to the Special Issue Emerging Technologies in Communications and Networking: 5G and Beyond)
Show Figures

Figure 1

Open AccessArticle
Cognitive Radio MANET Waveform Design and Evaluation
Sensors 2021, 21(4), 1052; https://doi.org/10.3390/s21041052 - 04 Feb 2021
Viewed by 199
Abstract
The problem of waveform construction for mobile ad hoc networks with cognitive radio (MANET-CR) is discussed. This is the main limitation to widely use this very attractive technique, which does not need the deployment of expensive communication infrastructure. Two main questions correspond to [...] Read more.
The problem of waveform construction for mobile ad hoc networks with cognitive radio (MANET-CR) is discussed. This is the main limitation to widely use this very attractive technique, which does not need the deployment of expensive communication infrastructure. Two main questions correspond to MANET-CR effectiveness: spectrum sensing and spectrum sharing. The paper presents the structure of CR nodes that enables Opportunistic Spectrum Sharing. Procedures for advanced Dynamic Spectrum Management together with the concept of policy-based radio and a sensing method are presented. In the proposed system, the basic policy is to avoid interference generated by other users or jammers. The experiments were performed in a real environment, using the elaborated testbed. The results show that the use of sensing and cognitive management mechanisms enable more efficient use of the spectrum while maintaining reasonable overhead values related to the management procedures. Full article
(This article belongs to the Special Issue Emerging Technologies in Communications and Networking: 5G and Beyond)
Show Figures

Figure 1

Open AccessCommunication
Machine Learning Enabled Performance Prediction Model for Massive-MIMO HetNet System
Sensors 2021, 21(3), 800; https://doi.org/10.3390/s21030800 - 26 Jan 2021
Viewed by 298
Abstract
To support upcoming novel applications, fifth generation (5G) and beyond 5G (B5G) wireless networks are being propelled to deploy an ultra-dense network with an ultra-high spectral efficiency using the combination of heterogeneous network (HetNet) solutions and massive Multiple Input Multiple Output (MIMO). As [...] Read more.
To support upcoming novel applications, fifth generation (5G) and beyond 5G (B5G) wireless networks are being propelled to deploy an ultra-dense network with an ultra-high spectral efficiency using the combination of heterogeneous network (HetNet) solutions and massive Multiple Input Multiple Output (MIMO). As the deployment of massive MIMO HetNet systems involves a high capital expenditure, network service providers need a precise performance analysis before investment. The performance of such networks is limited because of presence of inter-cell and inter-tier interferences. The conventional analytic approach to model the performance of such networks is not trivial, as the performance is a stochastic function of many network parameters. This paper proposes a machine learning (ML) approach to predict the network performance of a massive MIMO HetNet system considering a multi-cell scenario. This paper considers a two-tier network in which the base stations of each tier are equipped with massive MIMO systems working in a sub 6GHz band. The coverage probability (CP) and area spectral efficiency (ASE) are considered to be the network performance metrics that quantify the reliability and achievable rate in the network, respectively. Here, an ML model is inferred to predict the numerical values of the performance metrics for an arbitrary network configuration. In the process of practical deployments of future networks, the use of this model could be very valuable. Full article
(This article belongs to the Special Issue Emerging Technologies in Communications and Networking: 5G and Beyond)
Show Figures

Figure 1

Open AccessArticle
Modeling of Downlink Interference in Massive MIMO 5G Macro-Cell
Sensors 2021, 21(2), 597; https://doi.org/10.3390/s21020597 - 16 Jan 2021
Cited by 1 | Viewed by 499
Abstract
Multi-beam antenna systems are the basic technology used in developing fifth-generation (5G) mobile communication systems. In practical implementations of 5G networks, different approaches are used to enable a massive multiple-input-multiple-output (mMIMO) technique, including a grid of beams, zero-forcing, or eigen-based beamforming. All of [...] Read more.
Multi-beam antenna systems are the basic technology used in developing fifth-generation (5G) mobile communication systems. In practical implementations of 5G networks, different approaches are used to enable a massive multiple-input-multiple-output (mMIMO) technique, including a grid of beams, zero-forcing, or eigen-based beamforming. All of these methods aim to ensure sufficient angular separation between multiple beams that serve different users. Therefore, ensuring the accurate performance evaluation of a realistic 5G network is essential. It is particularly crucial from the perspective of mMIMO implementation feasibility in given radio channel conditions at the stage of network planning and optimization before commercial deployment begins. This paper presents a novel approach to assessing the impact of a multi-beam antenna system on an intra-cell interference level in a downlink, which is important for the accurate modeling and efficient usage of mMIMO in 5G cells. The presented analysis is based on geometric channel models that allow the trajectories of propagation paths to be mapped and, as a result, the angular power distribution of received signals. A multi-elliptical propagation model (MPM) is used and compared with simulation results obtained for a statistical channel model developed by the 3rd Generation Partnership Project (3GPP). Transmission characteristics of propagation environments such as power delay profile and antenna beam patterns define the geometric structure of the MPM. These characteristics were adopted based on the 3GPP standard. The obtained results show the possibility of using the presented novel MPM-based approach to model the required minimum separation angle between co-channel beams under line-of-sight (LOS) and non-LOS conditions, which allows mMIMO performance in 5G cells to be assessed. This statement is justified because for 80% of simulated samples of intra-cell signal-to-interference ratio (SIR), the difference between results obtained by the MPM and commonly used 3GPP channel model was within 2 dB or less for LOS conditions. Additionally, the MPM only needs a single instance of simulation, whereas the 3GPP channel model requires a time-consuming and computational power-consuming Monte Carlo simulation method. Simulation results of intra-cell SIR obtained this way by the MPM approach can be the basis for spectral efficiency maximization in mMIMO cells in 5G systems. Full article
(This article belongs to the Special Issue Emerging Technologies in Communications and Networking: 5G and Beyond)
Show Figures

Figure 1

Open AccessCommunication
Brain-Inspired Data Transmission in Dense Wireless Network
Sensors 2021, 21(2), 576; https://doi.org/10.3390/s21020576 - 15 Jan 2021
Viewed by 303
Abstract
In this paper, the authors investigate the innovative concept of a dense wireless network supported by additional functionalities inspired by the human nervous system. The nervous system controls the entire human body due to reliable and energetically effective signal transmission. Among the structure [...] Read more.
In this paper, the authors investigate the innovative concept of a dense wireless network supported by additional functionalities inspired by the human nervous system. The nervous system controls the entire human body due to reliable and energetically effective signal transmission. Among the structure and modes of operation of such an ultra-dense network of neurons and glial cells, the authors selected the most worthwhile when planning a dense wireless network. These ideas were captured, modeled in the context of wireless data transmission. The performance of such an approach have been analyzed in two ways, first, the theoretic limits of such an approach has been derived based on the stochastic geometry, in particular—based on the percolation theory. Additionally, computer experiments have been carried out to verify the performance of the proposed transmission schemes in four simulation scenarios. Achieved results showed the prospective improvement of the reliability of the wireless networks while applying proposed bio-inspired solutions and keeping the transmission extremely simple. Full article
(This article belongs to the Special Issue Emerging Technologies in Communications and Networking: 5G and Beyond)
Show Figures

Figure 1

Open AccessArticle
Analysis of the Single Frequency Network Gain in Digital Audio Broadcasting Networks
Sensors 2021, 21(2), 569; https://doi.org/10.3390/s21020569 - 14 Jan 2021
Viewed by 385
Abstract
The single frequency network (SFN) is a popular solution in modern digital audio and television system networks for extending effective coverage, compared to its traditional single-transmitter counterpart. As benefits of this configuration appear to be obvious, this paper focuses on the exact analysis [...] Read more.
The single frequency network (SFN) is a popular solution in modern digital audio and television system networks for extending effective coverage, compared to its traditional single-transmitter counterpart. As benefits of this configuration appear to be obvious, this paper focuses on the exact analysis of so-called SFN gain—a quantitative effect of advantage in terms of the received signal strength. The investigations cover a statistical analysis of SFN gain values, obtained by means of computer simulations, with respect to the factors influencing the coverage, i.e., the protection level, the reception mode (fixed, portable, mobile), and the receiver location (outdoor, indoor). The analyses conclude with an observation that the most noteworthy contribution of the SFN gain is observed on the far edges of the networks, and the least one close to the transmitters. It is also observed that the highest values of the SFN gain can be expected in the fixed mode, while the protection level has the lowest impact. Full article
(This article belongs to the Special Issue Emerging Technologies in Communications and Networking: 5G and Beyond)
Show Figures

Figure 1

Open AccessArticle
Stochastic Power Consumption Model of Wireless Transceivers
Sensors 2020, 20(17), 4704; https://doi.org/10.3390/s20174704 - 20 Aug 2020
Viewed by 475
Abstract
Energy efficiency is a key aspect when designing and optimizing contemporary wireless networks and transceivers. Assessment of energy efficiency requires proper energy consumption models. The most common solutions are to measure a single device and propose a device-specific model or to propose a [...] Read more.
Energy efficiency is a key aspect when designing and optimizing contemporary wireless networks and transceivers. Assessment of energy efficiency requires proper energy consumption models. The most common solutions are to measure a single device and propose a device-specific model or to propose a simplified model for many transceivers but not reflecting all phenomena visible in a given transceiver energy consumption. Therefore, it has to be selected to accurately model a single transceiver or coarsely model a wide group of transceivers. This paper proposes a new approach, where a fixed energy consumption model is used but with parameters being random variables. This reflects variability between various transceivers from various vendors. First the model parameters are adjusted separately for each of 14 measured WiFi modems. These devices are treated as samples of a wider population of devices and their parameters are used for stochastic parameters modeling, i.e., choosing the random variables’ distributions, their parameters, and the correlation among parameters. The proposed model can be used, e.g., for system-level network design where variability among transceivers power consumption can be used as a new degree of freedom. The paper presents simulation results for a simple multi-hop link whose energy consumption is characterized in much more detail thanks to the proposed stochastic power consumption model. Full article
(This article belongs to the Special Issue Emerging Technologies in Communications and Networking: 5G and Beyond)
Show Figures

Figure 1

Other

Jump to: Research

Open AccessLetter
Dynamic Transmit Profile Selection in Dense Wireless Networks
Sensors 2021, 21(1), 134; https://doi.org/10.3390/s21010134 - 28 Dec 2020
Viewed by 387
Abstract
The development of wireless networks can be characterized by both the increased number of deployed network nodes as well as their greater heterogeneity. As a consequence, the distance between the neighboring nodes decreases significantly, the density of such a wireless network is very [...] Read more.
The development of wireless networks can be characterized by both the increased number of deployed network nodes as well as their greater heterogeneity. As a consequence, the distance between the neighboring nodes decreases significantly, the density of such a wireless network is very high, and it brings to the mind the analogy to the human brain and nervous system, where a highly simplified scheme of information delivery is applied. Motivated by this similarity, in this paper, we study the possibility of the application of various transmission profiles in order to optimize the overall energy consumption in such dense wireless networks. The transmission profile specifies the radio access and energy consumption of the wireless transceiver (network node), and is characterized by the tuple of parameters, e.g., the total transmit power or minimal required signal-to-noise ratio (SNR). In the considered multi-hop network, we assume that each node can be set to the most promising transmission profile to achieve some predefined goals, such as (sensor) network reliability or transmission energy efficiency. We have proposed the new graph-based routing algorithm in such a dense wireless network, where total power consumption of message delivery is minimized by multihop and multimode transmission. The theoretical definition of the prospective transmission schemes is supported by the analysis of the results of the simulation experiments. Full article
(This article belongs to the Special Issue Emerging Technologies in Communications and Networking: 5G and Beyond)
Show Figures

Figure 1

Back to TopTop