1,721,168 research outputs found
A Comparative Study of the Computation Efficiency of a GPU-Based Ray Launching Algorithm for UAV-Assisted Wireless Communications
Full text linkGraphics Processing Units (GPU), have opened up new opportunities for speeding up general purpose parallel computing applications. In this paper, we present the computation efficiency in terms of time performances of a novel ray launching field prediction algorithm which relies on NVIDIA GPUs and its Compute Unified Device Architecture (CUDA). The software tool assesses the propagation losses between a wireless transmitter - carried by an Unmanned Air Vehicle (UAV) - over a 3D urban environment. Together with other effective features, the software tool is shown to reduce by several orders of magnitude the computation time of simulations. Performances and cost-benefit analysis of three different NVIDIA GPU configurations are thus investigated over three different urban scenarios, taken as test-cases for Air-to-Ground (A2G) communications for 5G applications and beyond
Metasurfaces as 6G Enabling Technology: A discussion on RIS applicability to Industrial IoT scenarios
No full textReconfigurable intelligent surfaces (RISs) are envisioned as keys enabling technologies for the future 6th generation (6G) to fulfil the extremely stringent requirements that Industrial IoT (IIoT) applications impose in terms of throughput, latency, and reliability. This paper discusses the real development and applicability of RIS in IIoT scenarios considering a trade-off between costs and performance improvement. The key performance indicators (KPIs) exploited to investigate the effectiveness of RIS planning are the path loss exponent (PLE) and the percentage of reachable nodes. The considered IIoT scenarios are corridors of different shape and elementary industrial plants
A Dual Frequency Blade Antenna Enabling UAV-Based Operations in ADS-B and 5G Environments
Full text linkDriven by the recent enhancements provided by
Automatic Dependent Surveillance-Broadcast (ADS-B) and the
latest developments in 5th Generation (5G) networks supported
by Unmanned Air Vehicles (UAVs), this paper describes the
design of an “all-in-one” SMA coaxial fed compact blade
antenna with dual frequency characteristics for broadband
applications on board of UAVs. A single antenna element is
designed using CST Microwave Studio software which shows a
dual frequency broadband characteristic, when compared to
traditional blade antennas, covering the 1.030 – 1.090 GHz and
the 3.4 – 3.8 GHz ranges thanks to an oblique side and a ‘C’
shaped cavity within the radiation element. The designed
antenna is simulated on an ideal ground plane first and then
extended to a bent ground plane. The results are compared and
discussed in terms of return loss, bandwidth, gain and radiation
pattern. These results show a lightweight antenna with a low
profile and a simple structure that can find numerous
applications in various airborne wideband communication
systems, suiting future UAV-based networks for 5G and
beyond while being perfectly compliant with the ongoing ADSB
based Detect-And-Avoid (DAA) technologies for integration
of UAVs in Unmanned Aerial Traffic Management (UTM)
A Machine Learning Approach to Wireless Propagation Modeling in Industrial Environment
No full textWireless channel properties in industrial environments can differ from residential or office settings due to the considerable impact of heavy machinery that triggers intricate multipath propagation effects and strong blockage effects. Previous investigations on wireless propagation in factories often consisted of empirical models, that is simple analytical formulas based on measurement data. Unfortunately, they usually lack in flexibility, since they seldom include geometrical parameters describing the industrial scenario and therefore turn out reliable only in industrial scenarios sharing the same propagation characteristics as those where the measurements were performed. In response to this limitation, this article harnesses the power of Machine Learning to model propagation markers like path loss, shadowing, and delay spread in the industrial environment. By employing Machine Learning techniques, the objective is to achieve flexibility and adaptability in modeling, enabling the system to effectively generalize across diverse industrial scenarios. The proposed model relies on a combination of predictive algorithms, including a linear regression model and a Multi-Layer Perceptron, working collaboratively to model the relationship between the considered propagation markers and input features like frequency and machine size, spacing, and density. Results are in fair overall agreement with previous studies and highlight some trends about the sensitivity of the propagation parameters to the considered input features
Smart metering wireless networks at 169 MHz
Full text linkIntelligent metering systems are being rolled-out on a large-scale worldwide, enabling consumer to make informed choices about consumption patterns and energy saving, while supporting the development of new retail services and products. Unfortunately, the lack of established and shared international standards represents a serious hindrance to be overcome for a complete development of a profitable market. The identification of suitable communication protocols and cost-effective network architectures represent a challenging aspect. In this framework, different network design solutions for wireless smart metering systems at 169 MHz are considered and investigated in this paper, aiming at cost efficient deployment based on extensive re-use of existing infrastructures in urban scenarios, namely, macro-cellular and lighting networks. Coverage assessment and frequency planning issues are addressed, together with an ad hoc measurement campaign carried out to fill the gap in the knowledge of urban propagation in the 169 MHz band. Results show that cost-effective deployment of the intelligent metering network is achievable. Notably, a spatial reuse factor larger than the overall number of available frequency channels might be necessary, thus meaning that the spectral resources shall be also allocated according to a time division scheme, where the hubs are switched off at turn. Anyway, this requirement should not affect the overall reading rate in practical applications. 2017 IEEE
A Reciprocal Heuristic Model for Diffuse Scattering from Walls and Surfaces
Full text linkDiffuse scattering of electromagnetic waves from natural and artificial
surfaces has been extensively studied in various disciplines, including radio
wave propagation, and several diffuse scattering models based on different
approaches have been proposed over the years, two of the most popular ones
being Kirchhoff Theory and the so-called Effective Roughness heuristic model.
The latter, although less rigorous than the former, is more flexible and
applicable to a wider range of real-world cases, including non-Gaussian
surfaces, surfaces with electrically small correlation lengths and scattering
from material inhomogeneities that are often present under the surface.
Unfortunately, the Effective Roughness model, with the exception of its
Lambertian version, does not satisfy reciprocity, which is an important
physical-soundness requirement for any propagation model. In the present work,
without compromising its effectiveness and its simple and yet sound
power-balance approach, we propose a reciprocal version of the Effective
Roughness model, which can be easily implemented and replaced to the old
version in ray-based propagation models. The new model is analyzed and compared
to the old one and to other popular models. Once properly calibrated, it is
shown to yield similar - if not better - performance with respect to the old
one when checked vs. measurements
Narrowband Characteristics of Air-to-Ground Propagation for UAV Assisted Networks in Urban Environments By Means of Fast Ray-Launching Simulations
No full textUnmanned Aerial Vehicles (UAV), also known as “drones”, are attracting increasing attention as enablers for many technical applications and services, and are emerging as a promising feature for constructing the next-generation mobile networks, with a special focus on the extension of coverage and capacity of mobile radio networks for 5G applications. In this paper we tackle this challenge and we aim at investigating the narrowband properties of the air-to-ground propagation channel by means of GPU accelerated ray launching simulations carried out in an urban environment for 5G communications
Analysis of Outdoor-to-Indoor Propagation at 169 MHz for Smart Metering Applications
No full textAn experimental work aimed at assessing the
outdoor-to-indoor propagation losses at 169 MHz is described in
this paper. The building penetration loss, often considered as an
additional constant value to be added to propagation losses in
previous studies, is here on the contrary regarded as a random
variable; its cumulative distribution is extracted from the measured
data and is found to be approximately Gaussian. Moreover,
in order to account for the critical installation conditions which
may be experienced by indoor wireless devices in particular applications
(e.g., wireless smart metering), an additional loss term,
here indicated as installation loss, is introduced and its value is
investigated in some reference cases. The achieved results are also
embedded into a statistical procedure similar to those commonly
adopted for wireless cellular networks planning
Line of Sight Detection in Industrial Environment: A Machine Learning Approach
No full textLine of Sight condition is usually beneficial in wireless communication links, as it commonly corresponds to better quality of service and can also simplify the reliable execution of tasks like beamforming and localization. Existing models dealing with line-of-sight detection are limited to statistical assessment, whic consists of line-of-sight probability formulas. In this work, a machine learning-based tool for point-to-point assessment of the line of sight condition is presented. The model is tailored to the industrial environment, where wireless technologies have been gaining increasing importance in the development of nextgeneration smart factories. Machine learning is leveraged to get flexibility, i.e. to evaluate the presence of line of sight not only depending on the link distance but also on some general descriptive features of the industrial scenario, like machine size and density. Results show good performance and the overall physical soundness of the tool
- …
