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Modeling and Measurements of Propagation Environments for 5G and beyond Networks

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

Deadline for manuscript submissions: closed (25 November 2023) | Viewed by 16696

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Prof. Dr. Jan Kelner

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Guest Editor

Institute of Communications Systems, Faculty of Electronics, Military University of Technology, Warsaw, Poland
Interests: wireless communications; radio emitter localization; radio navigation; flying ad hoc network (FANET); swarm; quality of service (QoS); channel modeling & measurements; radio wave propagation; multipath propagation; Doppler effect
Special Issues, Collections and Topics in MDPI journals

Prof. Dr. Cezary Ziółkowski

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Guest Editor

Institute of Communications Systems, Faculty of Electronics, Military University of Technology, Gen. Sylwester Kaliski St. No. 2, 00-908 Warsaw, Poland
Interests: channel modeling & measurements; radio wave propagation; multipath propagation; channel dispersion; doppler effect; radio emitter localization; radio navigation; electronic warfare

Prof. Dr. Aleš Prokeš

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Guest Editor

Department of Radio Electronics, Faculty of Electrical Engineering and Communication, Brno University of Technology, Technická 3082/12, 616 00 Brno, Czech Republic
Interests: RF channel measurement and modeling; V2X communication, localization and positioning; optimization of receivers for free-space optical systems; influence of atmospheric channel on optical signal propagation; higher order non-uniform sampling and signal reconstruction; software-defined radio
Special Issues, Collections and Topics in MDPI journals

Dr. Aniruddha Chandra

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Guest Editor

Electronics & Communication Engineering Department, National Institute of Technology, Durgapur 713209, India
Interests: mmWave for 5G; Vehicular communication; Free space optical communication

Special Issue Information

Dear Colleagues,

The initial analysis, design, and spatial planning of wireless networks are conditioned propagation environment property transmissions. Due to the complexity of phenomena, the practical assessment of propagation influence on the effectiveness of wireless transmission paths is based on simulation studies. This applies to both modern and upcoming (5G and beyond) cellular, satellite, Internet of Things (IoT), body area (BANs), vehicle-to-everything (V2X), ad-hoc mobile (MANETs), and wireless sensor networks (WSNs). In this case, adequate modeling of the system components (i.e., transmitter/receiver/sensor, base station/user equipment) and radio networks (i.e., higher layers of the ISO Open Systems Interconnection (OSI) Reference Model), properly reflecting the influence of the propagation environment, is crucial. It is worth highlighting that the propagation channel is one of the physical-layer elements that is significantly decisive for the efficiency of the designed radio links, networks, and entire systems. Hence, it follows that the application of the propagation models and channels verified in real conditions has a significant impact on the efficiency and determines the correctness of the planning process of wireless radio networks. Therefore, these models should ensure the reflection of propagation effects on a received-signal form, which corresponds to results obtained in actual conditions. This goal is achieved when the basis for channel modeling is propagation measurement results that are obtained under real environmental conditions. Such measurements should consider the specificity of the developed systems, including the used frequency ranges, environment character, parameters and patterns of antenna systems, etc.

5G and beyond networks will be based on millimeter-wave and terahertz bands. However, the emerging systems also use lower microwave ranges, including centimeter waves. However, path loss increases when wavelengths decrease. For this reason, multi-antenna systems (including massive-MIMO) are used, which enable beamforming, increasing their energy gain and the spatial multiplexing of radio resources. Therefore, the development of propagation and channel models for designing and planning the new emerging wireless networks, considering the patterns and parameters (i.e., gain, beamwidth, radiation/reception directions) of the antenna systems, is important.

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

Channel modeling for cellular, IoT, V2X, satellite networks, BANs, WSNs, MANETs, etc.;
Propagation measurements in the range of centimeter, millimeter, and terahertz waves;
Channel measurements and modeling for various environments (indoor/outdoor, urban street-canyon, etc.) and specific communication systems (e.g., V2X, IoT, high-speed train, highway, military MANETs, and WSNs);
Novel estimation methods of current channel state (i.e., parameters and transmission characteristics of channels) on the basis of measurement data;
Accuracy and error conditioning in estimating current state of channels;
Channel models in systems with a spatially limited propagation area by the use of beamforming, massive-MIMO, and multi-antenna systems;
Machine learning and artificial intelligence algorithms for propagation modeling;
Measurements and modeling of channel dispersion in time, frequency (i.e., Doppler effect), and reception angle domains,
Analytical, geometric, statistical, stochastic, or deterministic approaches to modeling stationary or time-varying channels;
Methods of signal processing and synthesis to determine channel transmission characteristics (channel impulse response, power delay profile, angular power spectrum, Doppler spectrum, etc.);
Parameters and transmission characteristics of propagation models and real measurements in effectiveness analysis (e.g., capacity, interference) of wireless links;
Channel models in the assessment of the coexistence and electromagnetic compatibility of different systems operating in the same frequency bands or adjacent channels;
Building of electromagnetic situation awareness in cognitive radio networks with the use of measurements or propagation modeling;
Designing, spatial planning, and modeling the emerging and future wireless networks considering channel model and propagation measurements.

Dr. Jan M. Kelner
Prof. Dr. Cezary Ziółkowski
Prof. Dr. Aleš Prokeš
Dr. Aniruddha Chandra
Guest Editors

Manuscript Submission Information

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Published Papers (7 papers)

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Research

17 pages, 5800 KiB

Open AccessArticle

Modification of Simple Antenna Pattern Models for Inter-Beam Interference Assessment in Massive Multiple-Input–Multiple-Output Systems

by Jarosław Wojtuń, Cezary Ziółkowski and Jan M. Kelner

Sensors 2023, 23(22), 9022; https://0-doi-org.brum.beds.ac.uk/10.3390/s23229022 - 07 Nov 2023

Viewed by 910

Abstract

The occurrence of cross-beam interference in the received signal is one of the main problems that limit the possibilities of massive multiple-input–multiple-output technology (massive-MIMO) in fifth-generation (5G) systems. Thus, the evaluation of the level of this interference is one of the most important procedures in the spatial planning of currently wireless networks. We propose a novel modification of simple antenna pattern models, which is based only on changing the directivity of real antenna system patterns. This approach is independent of the antenna system’s type, structure, and analytical description. Based on the developed modification, the original methodology for assessing the signal-to-interference ratio (SIR) from adjacent beams of a common antenna system is presented. The change in the radiation direction and the accompanying change in the complex shape and parameters of the real antenna beam pattern is one of the problems that significantly hinders the evaluation of the analyzed interference. Hence, in the presented methodology, we propose using our modification. In this case, the modification is reduced to a proportional change in the directivity concerning the real antenna system, which results from a change in the beam direction. The simulation studies used a multi-ellipsoidal propagation model and a real massive MIMO antenna pattern description from 3GPP. For the SIR error analysis, the 3GPP pattern is used as a reference. The simulation results show that modifying simple antenna pattern models allows us to obtain an SIR error of no more than 3 dB and 0.1 dB under line-of-sight (LOS) and non-LOS conditions, respectively. Full article

(This article belongs to the Special Issue Modeling and Measurements of Propagation Environments for 5G and beyond Networks)

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15 pages, 1894 KiB

Open AccessArticle

A Mobility Model for a 3D Non-Stationary Geometry Cluster-Based Channel Model for High Speed Trains in MIMO Wireless Channels

by Eva Assiimwe and Yihenew Wondie Marye

Sensors 2022, 22(24), 10019; https://0-doi-org.brum.beds.ac.uk/10.3390/s222410019 - 19 Dec 2022

Cited by 2 | Viewed by 1574

Abstract

During channel modeling for high-mobility channels, such as high-speed train (HST) channels, the velocity of the mobile radio station is assumed to be constant. However, this might not be realistic due to the dynamic movement of the train along the track. Therefore, in this paper, an enhanced Gauss–Markov mobility model with a 3D non-stationary geometry based stochastic model (GBSM) for HST in MIMO Wireless Channels is proposed. The non-isotropic scatterers within a cluster are assumed to be around the sphere in which the mobile relay station (MRS) is located. The multi-path components (MPCs) are modeled with varying velocities, whereas the mobility model is a function of time. The MPCs are represented in a death–birth cluster using the Markov process. Furthermore, the channel statistics, i.e., the space-time correlation function, the root-mean-square Doppler shift, and the quasi-stationary interval, are derived from the non-stationary model. The model shows how the quasi-stationary time increases from 0.21 to 0.451 s with a decreasing acceleration of 0.6 to 0.2 m/s

^{2}

of the HST. In addition, the impact of the distribution of the angles on the channel statistics is presented. Finally, the simulated results are compared with the measured results. Therefore, there is a close relationship between the proposed model and the measured results, and the model can be used to characterize the channel’s properties. Full article

(This article belongs to the Special Issue Modeling and Measurements of Propagation Environments for 5G and beyond Networks)

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20 pages, 10691 KiB

Open AccessArticle

Propagation Attenuation Maps Based on Parabolic Equation Method

by Michał Kryk, Krzysztof Malon and Jan M. Kelner

Sensors 2022, 22(11), 4063; https://0-doi-org.brum.beds.ac.uk/10.3390/s22114063 - 27 May 2022

Cited by 7 | Viewed by 1927

Abstract

Modern wireless communication systems use various technological solutions to increase the efficiency of created radio networks. This efficiency also applies to radio resources. Currently, the utilization of a radio environment map (REM) is one of the directions allowing to improve radio resource management. The REM is increasingly used in emerging mobile ad-hoc networks (MANETs), in particular military tactical networks. In this case, the use of new technologies such as software-defined radio and network, cognitive radio, radio sensing, and building electromagnetic situational awareness made it possible to implement REM in tactical MANETs. Propagation attenuation maps (PAMs) are crucial REM elements that allow for determining the ranges of radio network nodes. In this paper, we present a novel algorithm for PAM based on a parabolic equation method (PEM). The PEM allows determining the signal attenuation along the assumed propagation direction. In this case, we consider terrain topography to obtain a more realistic analysis. Then, we average the adjacent attenuation profiles defined for the selected directions in places where attenuation has not been calculated. To this aim, linear regression is applied. Finally, we define several metrics that allow for the accuracy assessment of determining the PAM as a function of its dimensions. Full article

(This article belongs to the Special Issue Modeling and Measurements of Propagation Environments for 5G and beyond Networks)

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18 pages, 31155 KiB

Open AccessArticle

Building the Electromagnetic Situation Awareness in MANET Cognitive Radio Networks for Urban Areas

by Paweł Skokowski, Krzysztof Malon and Jerzy Łopatka

Sensors 2022, 22(3), 716; https://0-doi-org.brum.beds.ac.uk/10.3390/s22030716 - 18 Jan 2022

Cited by 10 | Viewed by 1775

Abstract

This paper presents a solution for building awareness of the electromagnetic situation in cognitive mobile ad hoc networks (MANET) using the cooperative spectrum sensing method. Signal detection is performed using energy detectors with noise level estimation. Based on the evidence theory, the fusion center decides on the particular channel occupancy, which can process incomplete and unambiguous input data. Next, a reinforced machine learning algorithm estimates the usefulness of particular channels for the MANET transmission and creates backup channels list that could be used in case of interferences. Initial simulations were performed using the MATLAB environment, and next an OMNET-based MAENA high fidelity simulator was used. Performed simulations showed a significant increase in sensing efficiency compared to sensing performed using simple data fusion rules. Full article

(This article belongs to the Special Issue Modeling and Measurements of Propagation Environments for 5G and beyond Networks)

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20 pages, 5858 KiB

Open AccessArticle

Radio Channel Capacity with Directivity Control of Antenna Beams in Multipath Propagation Environment

by Cezary Ziółkowski, Jan M. Kelner, Jarosław Krygier, Aniruddha Chandra and Aleš Prokeš

Sensors 2021, 21(24), 8296; https://0-doi-org.brum.beds.ac.uk/10.3390/s21248296 - 11 Dec 2021

Cited by 2 | Viewed by 2629

Abstract

The basic technology that will determine the expansion of the technical capabilities of fifth generation cellular systems is a massive multiple-input-multiple-output. Therefore, assessing the influence of the antenna beam orientations on the radio channel capacity is very significant. In this case, the effects of mismatching the antenna beam directions are crucial. In this paper, the methodology for evaluating changes in the received signal power level due to beam misalignment for the transmitting and receiving antenna systems is presented. The quantitative assessment of this issue is presented based on simulation studies carried out for an exemplary propagation scenario. For non-line-of-sight (NLOS) conditions, it is shown that the optimal selection of the transmitting and receiving beam directions may ensure an increase in the level of the received signal by several decibels in relation to the coaxial position of the beams. The developed methodology makes it possible to analyze changes in the radio channel capacity versus the signal-to-noise ratio and distance between the transmitter and receiver at optimal and coaxial orientations of antenna beams for various propagation scenarios, considering NLOS conditions. In the paper, the influence of the directional antenna use and their direction choices on the channel capacity versus SNR and the distance between the transmitter and receiver is shown. Full article

(This article belongs to the Special Issue Modeling and Measurements of Propagation Environments for 5G and beyond Networks)

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18 pages, 5988 KiB

Open AccessArticle

Results of Large-Scale Propagation Models in Campus Corridor at 3.7 and 28 GHz

by Md Abdus Samad, Feyisa Debo Diba, Young-Jin Kim and Dong-You Choi

Sensors 2021, 21(22), 7747; https://0-doi-org.brum.beds.ac.uk/10.3390/s21227747 - 21 Nov 2021

Cited by 14 | Viewed by 2363

Abstract

The indoor application of wave propagation in the 5G network is essential to fulfill the increasing demands of network access in an indoor environment. This study investigated the wave propagation properties of line-of-sight (LOS) links at two long corridors of Chosun University (CU). We chose wave propagation measurements at 3.7 and 28 GHz, since 3.7 GHz is the closest to the roll-out frequency band of 3.5 GHz in South Korea and 28 GHz is next allocated frequency band for Korean telcos. In addition, 28 GHz is the promising millimeter band adopted by the Federal Communications Commission (FCC) for the 5G network. Thus, the 5G network can use 3.7 and 28 GHz frequencies to achieve the spectrum required for its roll-out frequency band. The results observed were applied to simulate the path loss of the LOS links at extended indoor corridor environments. The minimum mean square error (MMSE) approach was used to evaluate the distance and frequency-dependent optimized coefficients of the close-in (CI) model with a frequency-weighted path loss exponent (CIF), floating-intercept (FI), and alpha–beta–gamma (ABG) models. The outcome shows that the large-scale FI and CI models fitted the measured results at 3.7 and 28 GHz. Full article

(This article belongs to the Special Issue Modeling and Measurements of Propagation Environments for 5G and beyond Networks)

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24 pages, 8348 KiB

Open AccessArticle

A Deep Neural Network-Based Multi-Frequency Path Loss Prediction Model from 0.8 GHz to 70 GHz

by Chi Nguyen and Adnan Ahmad Cheema

Sensors 2021, 21(15), 5100; https://0-doi-org.brum.beds.ac.uk/10.3390/s21155100 - 28 Jul 2021

Cited by 21 | Viewed by 3365

Abstract

Large-scale fading models play an important role in estimating radio coverage, optimizing base station deployments and characterizing the radio environment to quantify the performance of wireless networks. In recent times, multi-frequency path loss models are attracting much interest due to their expected support for both sub-6 GHz and higher frequency bands in future wireless networks. Traditionally, linear multi-frequency path loss models like the ABG model have been considered, however such models lack accuracy. The path loss model based on a deep learning approach is an alternative method to traditional linear path loss models to overcome the time-consuming path loss parameters predictions based on the large dataset at new frequencies and new scenarios. In this paper, we proposed a feed-forward deep neural network (DNN) model to predict path loss of 13 different frequencies from 0.8 GHz to 70 GHz simultaneously in an urban and suburban environment in a non-line-of-sight (NLOS) scenario. We investigated a broad range of possible values for hyperparameters to search for the best set of ones to obtain the optimal architecture of the proposed DNN model. The results show that the proposed DNN-based path loss model improved mean square error (MSE) by about 6 dB and achieved higher prediction accuracy R2 compared to the multi-frequency ABG path loss model. The paper applies the XGBoost algorithm to evaluate the importance of the features for the proposed model and the related impact on the path loss prediction. In addition, the effect of hyperparameters, including activation function, number of hidden neurons in each layer, optimization algorithm, regularization factor, batch size, learning rate, and momentum, on the performance of the proposed model in terms of prediction error and prediction accuracy are also investigated. Full article

(This article belongs to the Special Issue Modeling and Measurements of Propagation Environments for 5G and beyond Networks)

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