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7 pages, 1722 KiB  
Proceeding Paper
Analysing the Actual Use of Controller–Pilot Data Link Communications
by Erwin Orye, Gabor Visky and Olaf Maennel
Eng. Proc. 2022, 28(1), 18; https://doi.org/10.3390/engproc2022028018 - 28 Jan 2023
Viewed by 2062
Abstract
Controller–pilot data link communications (CPDLC) is a digital protocol and part of air navigation systems where Air Traffic Control (ATC) can text an aircraft instead of engaging in voice communications. This research used a CPDLC receiver at the airport of a European capital [...] Read more.
Controller–pilot data link communications (CPDLC) is a digital protocol and part of air navigation systems where Air Traffic Control (ATC) can text an aircraft instead of engaging in voice communications. This research used a CPDLC receiver at the airport of a European capital -EETN and captured all the CPDLC messages for one year. The total number of messages, more than 4.7 million might not be directly relevant since the COVID-19 pandemic affected the number of flights during that period. Still, the classification of the captured messages reveals the usage of this communication channel. Some characterisation analysis of the data traffic shows that only 2% of the 4.7 million messages are Connection-Oriented Transport Protocol (COTP) messages. If we do not consider the messages necessary to connect and establish a connection (e.g., next data authority, release request), there were, in the downlink, from aircraft to ATC, 4626 “wilco” messages, 74 free text messages, and 225 other messages. We found 6357 instruction messages and 9991 free text messages in the uplink. Therefore, only 0.3% of all VDL-2 messages have an operational added value. This enormous overhead, the limited available bandwith and the predicted increase of users of CPDLC, such as unmanned aircraft and recreative flights will saturate this completely this communication channel. Full article
(This article belongs to the Proceedings of The 10th OpenSky Symposium)
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Figure 1
<p>Test setup for catching ATN—VDL2 messages.</p>
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<p>Example of captured CPDLC message.</p>
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<p>SQL table interconnections.</p>
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<p>List of SQL tables.</p>
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20 pages, 18366 KiB  
Article
Design of 1 × 2 MIMO Palm Tree Coplanar Vivaldi Antenna in the E-Plane with Different Patch Structure
by Nurhayati Nurhayati, Eko Setijadi, Alexandre Maniçoba de Oliveira, Dayat Kurniawan and Mohd Najib Mohd Yasin
Electronics 2023, 12(1), 177; https://doi.org/10.3390/electronics12010177 - 30 Dec 2022
Cited by 2 | Viewed by 2566
Abstract
In this paper, 1 × 2 MIMO of Palm Tree Coplanar Vivaldi Antenna is presented that simulated at 0.5–4.5 GHz. Some GPR applications require wideband antennas starting from a frequency below 1 GHz to overcome high material loss and achieve deeper penetration. However, [...] Read more.
In this paper, 1 × 2 MIMO of Palm Tree Coplanar Vivaldi Antenna is presented that simulated at 0.5–4.5 GHz. Some GPR applications require wideband antennas starting from a frequency below 1 GHz to overcome high material loss and achieve deeper penetration. However, to boost the gain, antennas are set up in MIMO and this is costly due to the large size of the antenna. When configuring MIMO antenna in the E-plane, there is occasionally uncertainty over which antenna model may provide the optimum performance in terms of return loss, mutual coupling, directivity, beam squint, beam width, and surface current using a given substrate size. However, the configuration of E-plane antenna in MIMO has an issue of mutual coupling if the distance between elements is less than 0.5λ. Furthermore, it produces grating lobes at high frequencies.We implement several types of patch structures by incorporating the truncated, tilt shape, Hlbert and Koch Fractal, Exponential slot, Wave slot, the lens with elips, and metamaterial slot to the radiator by keeping the width of the substrate and the shape of the feeder. The return loss, mutual coupling, directivity, beam squint, beamwidth, and surface current of the antenna are compared for 1 × 2 MIMO CVA. A continuous patch MIMO has a spacing of 0.458λ at 0.5 GHz, which is equivalent to its element width. From the simulation, we found that Back Cut Palm Tree (BCPT) and Horizontale Wave Structure Palm Tree (HWSPT) got the best performance of return loss and mutual scattering at low-end frequency respectively. The improvement of directivity got for Metamaterial Lens Palm Tree (MLPT) of 4.453 dBi if compared with Regular Palm Tree-Coplanar Vivaldi Antena (RPT) at 4 GHz. Elips Lens Palm Tree (ELPT) has the best beam squint performance across all frequencies of 0°. It also gots the best beamwidth at 4.5 GHz of 3.320. In addition, we incorporate the MLPT into the radar application. Full article
(This article belongs to the Topic Antennas)
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Figure 1

Figure 1
<p>The 1 × 2 Coplanar Vivaldi Antenna of (<b>a</b>) Regular Palm Tree (RPT-CVA), (<b>b</b>) Front Cut Palm Tree (FCPT-CVA), (<b>c</b>) Middle Cut Palm Tree (MCPT-CVA), (<b>d</b>) Back Cut Palm Tree (BCPT-CVA), (<b>e</b>) Complete Cut Palm Tree (CCPT-CVA), (<b>f</b>) Left Tilt Palm Tree (LTPT-CVA), (<b>g</b>) Right Tilt Palm Tree (RTPT-CVA), (<b>h</b>) Hilbert Fractal Structure Palm Tree (HFSPT-CVA).</p>
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<p>The 1 × 2 MIMO Coplanar Vivaldi Antenna of (<b>a</b>) Koch Fractal Structure Palm Tree (KFSPT-CVA), (<b>b</b>) Exponential Slot Edge Palm Tree (ESEPT-CVA), (<b>c</b>) Vertical Wave Structure Palm Tree (VWPT-CVA), (<b>d</b>) Horizontale Wave Structure Palm Tree (HWPT-CVA), (<b>e</b>) Regular Lens Palm Tree (RLPT-CVA), (<b>f</b>) Elips Lens Palm Tree (ELPT-CVA), (<b>g</b>) Metamaterial Lens Palm Tree (MLPT-CVA).</p>
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<p><span class="html-italic">S</span><sub>11</sub> and <span class="html-italic">S</span><sub>21</sub> performance of 1 × 2 MIMO (<b>a</b>). Regular Palm Tree Coplanar Vivaldi Antena (RPT-CVA), Front Cut Palm Tree (FCPT-CVA), Middle Cut Palm Tree (MCPT-CVA), and (<b>b</b>). <span class="html-italic">S</span><sub>11</sub> and <span class="html-italic">S</span><sub>21</sub> of Regular Palm Tree Coplanar Vivaldi Antena (RPT-CVA), Back Cut Palm Tree (BCPT-CVA), Complete Cut Palm Tree (CCPT-CVA).</p>
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<p><span class="html-italic">S</span><sub>11</sub> and <span class="html-italic">S</span><sub>21</sub> performance of 1 × 2 MIMO (<b>a</b>). Regular Palm Tree-Coplanar Vivaldi Antena (RPT-CVA), Left Tilt Palm Tree (LTPT-CVA), Right Tilt Palm Tree (RTPT-CVA) and (<b>b</b>). Regular Palm Tree-Coplanar Vivaldi Antena (RPT-CVA), Hilbert Fractal Structure Palm Tree (HFSPT-CVA), Koch Fractal Structure Palm Tree (KFSPT-CVA).</p>
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<p><span class="html-italic">S</span><sub>11</sub> and <span class="html-italic">S</span><sub>21</sub> performance of 1 × 2 MIMO (<b>a</b>). Regular Palm Tree-Coplanar Vivaldi Antena (RPT-CVA), Exponential Slot Edge Palm Tree (ESEPT-CVA), Vertical Wave Structure Palm Tree (VWPT-CVA), and Horizontale Wave Structure Palm Tree (HWPTCVA) and (<b>b</b>) <span class="html-italic">S</span><sub>11</sub> and <span class="html-italic">S</span><sub>21</sub> of Regular Palm Tree-Coplanar Vivaldi Antena (RPT-CVA), Elips Lens Palm Tree (ELPT-CVA), and Metamaterial Lens Palm Tree (MLPT-CVA).</p>
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<p>Directivity of: (<b>a</b>). Single and 1 × 2 Regular Palm Tree (RPT-CVA), 1 × 2Front Cut Palm Tree (FCPT-CVA), 1 × 2 Middle Cut Palm Tree (MCPT-CVA), 1 × 2 Back Cut Palm Tree (BCPT-CVA), 1 × 2 Complete Cut Palm Tree (CCPT-CVA) and (<b>b</b>). Single and 1 × 2 of Regular Palm Tree (RPT-CVA), 1 × 2 Left Tilt Palm Tree (LTPT-CVA), 1 × 2 Right Tilt Palm Tree (RTPT-CVA), 1 × 2 Hilbert Fractal Structure Palm Tree (HFSPT-CVA), 1 × 2 Koch Fractal Structure Palm Tree (KFSPT-CVA).</p>
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<p>Directivity of (<b>a</b>). Element and 1 × 2 Regular Palm Tree- (RPT-CVA), 1 × 2 Exponential Slot Edge Palm Tree (ESE-CVA), 1 × 2 Vertical Wave Structure Palm Tree (VWPT-CVA), 1 × 2 Horizontale Wave Structure Palm Tree (HWPT-CVA), and (<b>b</b>). Element and 1 × 2 Regular Palm Tree-Coplanar Vivaldi Antena (RPT-CVA), 1 × 2 Regular Lens Palm Tree (RLPT-CVA), 1 × 2 Elips Lens Palm Tree (ELPT-CVA), And 1 × 2 Metamaterial Lens Palm Tree (MLPT-CVA).</p>
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<p>Side Lobe Level of (<b>a</b>). Element and 1 × 2 Regular Palm Tree (RPT-CVA), 1 × 2 Front Cut Palm Tree (FCPT-CVA), 1 × 2 Middle Cut Palm Tree (MCPT-CVA), 1 × 2 Back Cut Palm Tree (BCPT-CVA), 1 × 2 Complete Cut Palm Tree (CCPT-CVA), 1 × 2 Left Tilt Palm Tree (LTPT-CVA), 1 × 2 Right Tilt Palm Tree (RTPT-CVA), 1 × 2 Hilbert Fractal Structure Palm Tree (HFSPT-CVA), and (<b>b</b>). Element and 1 × 2 Regular Palm Tree (RPT-CVA), 1 × 2 Koch Fractal Structure Palm Tree (KFSPT-CVA), 1 × 2 Exponential Slot Edge Palm Tree (ESEPT-CVA), 1 × 2 Vertical Wave Structure Palm Tree (VWSPT-CVA), 1 × 2 Horizontale Wave Structure Palm Tree (HWSPT-CVA), 1 × 2 Regular Lens Palm Tree (RLPT-CVA), 1 × 2 Elips Lens Palm Tree (ELPT-CVA), and 1 × 2 Metamaterial Lens Palm Tree (MLPT-CVA).</p>
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<p>Beam Squint Performance of (<b>a</b>) Element and 1 × 2 Regular Palm Tree (RPT-CVA), 1 × 2 Front Cut Palm Tree (FCPT-CVA), 1 × 2 Middle Cut Palm Tree (MCPT-CVA), 1 × 2 Back Cut Palm Tree (BCPT-CVA), 1 × 2 Complete Cut Palm Tree (CCPT-CVA), 1 × 2 Left Tilt Palm Tree (LTPT-CVA), Right Tilt Palm Tree (RTPT-CVA), 1 × 2 Hilbert Fractal Structure Palm Tree (HFSPT-CVA), and (<b>b</b>). Element and 1 × 2 Regular Palm Tree (RPT-CVA), 1 × 2 Koch Fractal Structure Palm Tree (KFSPT-CVA), 1 × 2 Exponential Slot Edge Palm Tree (ESEPT-CVA), 1 × 2 Vertical Wave Structure Palm Tree (VWSPT-CVA), 1 × 2 Horizontale Wave Structure Palm Tree (HWSPT-CVA), 1 × 2 Regular Lens Palm Tree (RLPT-CVA), 1 × 2 Elips Lens Palm Tree (ELPT-CVA), and 1 × 2 Metamaterial Lens Palm Tree (MLPT-CVA).</p>
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<p>Beamwidth of (<b>a</b>). Element and 1 × 2 Regular Palm Tree (RPT-CVA), 1 × 2 Front Cut Palm Tree (FCPT-CVA), 1 × 2 Middle Cut Palm Tree (MCPT-CVA), 1 × 2 Back Cut Palm Tree (BCPT-CVA), 1 × 2 Complete Cut Palm Tree (CCPT-CVA), 1 × 2 Left Tilt Palm Tree (LTPT-CVA), 1 × 2 Right Tilt Palm Tree (RTPT-CVA), 1 × 2 Hilbert Fractal Structure Palm Tree (HFSPT-CVA), and (<b>b</b>). Element and 1 × 2 Regular Palm Tree (RPT-CVA), 1 × 2 Koch Fractal Structure Palm Tree (KFSPT-CVA), 1 × 2 Exponential Slot Edge Palm Tree (ESEPT-CVA), 1 × 2 Vertical Wave Structure Palm Tree (VWSPT-CVA), 1 × 2 Horizontale Wave Structure Palm Tree (HWSPT-CVA), 1 × 2 Regular Lens Palm Tree (RLPT-CVA), 1 × 2 Elips Lens Palm Tree (ELPT-CVA), and 1 × 2 Metamaterial Lens Palm Tree (MLPT-CVA).</p>
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<p>Radiation Pattern in the E-Plane of (<b>a</b>). RPT-CVA vs FCPT-CVA at 2 GHz, (<b>b</b>). RPT-CVA vs HFSPT-CVA at 2 GHz, (<b>c</b>) RPT-CVA vs ESE-CVA at 4 GHz, and (<b>d</b>). RPT-CVA vs MLPT-CVA at 4 GHz.</p>
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<p>Surface current performance of (<b>a</b>) Regular Palm Tree (RPT-CVA), (<b>b</b>) Front Cut Palm Tree (FCPT-CVA), (<b>c</b>) Back Cut Palm Tree (BCPT-CVA), (<b>d</b>) Hilbert Fractal Structure Palm Tree (HFSPT-CVA), (<b>e</b>) Vertical Wave Structure Palm Tree (VWSPT-CVA), (<b>f</b>) Horizontale Wave Structure Palm Tree (HWSPT-CVA), (<b>g</b>) Exponential Slot Edge Palm Tree (ESEPT-CVA), (<b>h</b>) Elips Lens Palm Tree (ELPT-CVA), and (<b>i</b>) Metamaterial Lens Palm Tree (MLPT-CVA).</p>
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<p>Surface current performance of (<b>a</b>) Regular Palm Tree (RPT-CVA), (<b>b</b>) Front Cut Palm Tree (FCPT-CVA), (<b>c</b>) Middle Cut Palm Tree (MCPT-CVA), (<b>d</b>) Back Cut Palm Tree (BCPT-CVA), (<b>e</b>) Complete Cut Palm Tree (CCPT-CVA), (<b>f</b>) Left Tilt Palm Tree (LTPT-CVA), (<b>g</b>) Right Tilt Palm Tree (RTPT-CVA), (<b>h</b>) Hilbert Fractal Structure Palm Tree (HFSPT-CVA), (<b>i</b>) Koch Fractal Structure Palm Tree (KFSPT-CVA), (<b>j</b>) Exponential Slot Edge Palm Tree (ESEPT-CVA), (<b>k</b>) Vertical Wave Structure Palm Tree (VWSPT-CVA), (<b>l</b>) Horizontale Wave Structure Palm Tree (HWSPT-CVA), (<b>m</b>) Regular Lens Palm Tree (RLPT-CVA), (<b>n</b>) Elips Lens Palm Tree (ELPT-CVA), and (<b>o</b>) Metamaterial Lens Palm Tree (MLPT-CVA).</p>
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<p>Simulation and measurement result of (<b>a</b>) ESEPT-CVA and (<b>b</b>) MLPT-CVA.</p>
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<p>Radar target measurement with MLPT-CVA prototype.</p>
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<p>Radar target detection in the xy-planes.</p>
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15 pages, 2660 KiB  
Article
Path Loss Model for Outdoor Parking Environments at 28 GHz and 38 GHz for 5G Wireless Networks
by Ahmed M. Al-Samman, Tharek Abd Rahman, MHD Nour Hindia, Abdusalama Daho and Effariza Hanafi
Symmetry 2018, 10(12), 672; https://doi.org/10.3390/sym10120672 - 29 Nov 2018
Cited by 18 | Viewed by 3845
Abstract
It has been widely speculated that the performance of the next generation Internet of Things (IoT) based wireless network should meet a transmission speed on the order of 1000 times more than current wireless networks; energy consumption on the order of 10 times [...] Read more.
It has been widely speculated that the performance of the next generation Internet of Things (IoT) based wireless network should meet a transmission speed on the order of 1000 times more than current wireless networks; energy consumption on the order of 10 times less and access delay of less than 1 ns that will be provided by future 5G systems. To increase the current mobile broadband capacity in future 5G systems, the millimeter wave (mmWave) band will be used with huge amounts of bandwidth available in this band. Hence, to support this wider bandwith at the mmWave band, new radio access technology (RAT) should be provided for 5G systems. The new RAT with symmetry design for downlink and uplink should support different scenarios such as device to device (D2D) and multi-hop communications. This paper presents the path loss models in parking lot environment which represents the multi-end users for future 5G applications. To completely assess the typical performance of 5G wireless network systems across these different frequency bands, it is necessary to develop path loss (PL) models across these wide frequency ranges. The short wavelength of the highest frequency bands provides many scatterings from different objects. Cars and other objects are some examples of scatterings, which represent a critical issue at millimeter-wave bands. This paper presents the large-scale propagation characteristics for millimeter-wave in a parking lot environment. A new physical-based path loss model for parking lots is proposed. The path loss was investigated based on different models. The measurement was conducted at 28 GHz and 38 GHz frequencies for different scenarios. Results showed that the path loss exponent values were approximately identical at 28 GHz and 38 GHz for different scenarios of parking lots. It was found that the proposed compensation factor varied between 10.6 dB and 23.1 dB and between 13.1 and 19.1 in 28 GHz and 38 GHz, respectively. The proposed path loss models showed that more compensation factors are required for more scattering objects, especially at 28 GHz. Full article
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<p>Measurement equipment.</p>
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<p>Measurement environment: (<b>a</b>) photo of parking lots; (<b>b</b>) floor plan of the environment.</p>
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<p>Close-in (CI) free space reference distance path loss model at 28 GHz.</p>
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<p>CI path loss model at 38 GHz.</p>
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<p>Floating-intercept (FI) path loss model at 28 GHz.</p>
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<p>FI path loss model at 38 GHz.</p>
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<p>The proposed parking lot path loss model (PLM) at 28 GHz.</p>
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<p>The proposed PLM at 38 GHz.</p>
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<p>The alpha-beta-gamma (ABG) path loss model for 28 GHz and 38 GHz. 6.2 Numerical Results and Computation Complexity.</p>
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<p>Predicted received power at 28 GHz.</p>
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<p>Predicted received power at 38 GHz.</p>
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965 KiB  
Article
Smart Bandwidth Assignation in an Underlay Cellular Network for Internet of Vehicles
by Idoia De la Iglesia, Unai Hernandez-Jayo, Eneko Osaba and Roberto Carballedo
Sensors 2017, 17(10), 2217; https://doi.org/10.3390/s17102217 - 27 Sep 2017
Cited by 14 | Viewed by 4873
Abstract
The evolution of the IoT (Internet of Things) paradigm applied to new scenarios as VANETs (Vehicular Ad Hoc Networks) has gained momentum in recent years. Both academia and industry have triggered advanced studies in the IoV (Internet of Vehicles), which is understood as [...] Read more.
The evolution of the IoT (Internet of Things) paradigm applied to new scenarios as VANETs (Vehicular Ad Hoc Networks) has gained momentum in recent years. Both academia and industry have triggered advanced studies in the IoV (Internet of Vehicles), which is understood as an ecosystem where different types of users (vehicles, elements of the infrastructure, pedestrians) are connected. How to efficiently share the available radio resources among the different types of eligible users is one of the important issues to be addressed. This paper briefly analyzes various concepts presented hitherto in the literature and it proposes an enhanced algorithm for ensuring a robust co-existence of the aforementioned system users. Therefore, this paper introduces an underlay RRM (Radio Resource Management) methodology which is capable of (1) improving cellular spectral efficiency while making a minimal impact on cellular communications and (2) ensuring the different QoS (Quality of Service) requirements of ITS (Intelligent Transportation Systems) applications. Simulation results, where we compare the proposed algorithm to the other two RRM, show the promising spectral efficiency performance of the proposed RRM methodology. Full article
(This article belongs to the Special Issue Next Generation Wireless Technologies for Internet of Things)
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Figure 1
<p>Overview of the system.</p>
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<p>Resource Allocation steps.</p>
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<p>Representation of pairing constraints. (<b>a</b>) possible pair; (<b>b</b>) impossible pair.</p>
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<p>Throughput offered to V-UEs based on C-UEs’ traffic pattern. (<b>a</b>) Average Throughput of V-UEs in different scenarios; (<b>b</b>) Total Average Throughput of V-UEs.</p>
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<p>Throughput offered to V-UEs based on C-UEs’ traffic pattern. (<b>a</b>) Total Average Throughput in different scenarios; (<b>b</b>) Total Average Throughput for all set of users.</p>
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<p>Spectral Efficiency offered to V-UEs based on C-UEs’ traffic pattern. (<b>a</b>) Total Average Spectral Efficiency for V-UEs in different scenarios; (<b>b</b>) Total Average Spectral Efficiency for V-UEs.</p>
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<p>Spectral Efficiency offered to V-UEs based on C-UEs’ traffic pattern. (<b>a</b>) Total Average Spectral Efficiency in different scenarios; (<b>b</b>) Total Average Spectral Efficiency for all set of users.</p>
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<p>Energy Efficiency offered to V-UEs based on C-UEs’ traffic pattern. (<b>a</b>) Total Average Energy Efficiency for V-UEs in different scenarios; (<b>b</b>) Total Average Energy Efficiency for V-UEs.</p>
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<p>Energy Efficiency offered to V-UEs based on C-UEs’ traffic pattern. (<b>a</b>) Total Average Energy Efficiency in different scenarios; (<b>b</b>) Total Average Energy Efficiency for all set of users.</p>
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5387 KiB  
Article
Neural Computing of the Bandwith of Resonant Rectangular Microstrip Antennas
by Şeref Sağıroğlu, Kerim Güney and Mehmet Erler
Math. Comput. Appl. 1998, 3(1), 37-47; https://doi.org/10.3390/mca3010037 - 1 Apr 1998
Viewed by 1172
Abstract
A new method based on the backpropagation multilayered perceptron network for calculating the bandwidth of resonant rectangular microstrip patch antennas is presented The method can be used for a wide range of substrate thicknesses and permittivities, and is useful for the computer-aided design [...] Read more.
A new method based on the backpropagation multilayered perceptron network for calculating the bandwidth of resonant rectangular microstrip patch antennas is presented The method can be used for a wide range of substrate thicknesses and permittivities, and is useful for the computer-aided design (CAD) of microstrip antennas. The results obtained by using this new method are in conformity with those reported elsewhere. This method may find wide applications in high-frequency printed antennas, especially at the millimeter-wave frequency range. Full article
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