Impact of Antenna Tilting on Propagation Shadowing Model

Kifle, Dereje W.; Wegmann, Bernhard; Viering, Ingo; Klein, Anja

doi:10.1109/vtcspring.2013.6692585

Cited by 6 publications

(4 citation statements)

References 6 publications

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“…Our research work is different from the above because we have taken the OLSR protocol and also different models. Kifle et al, (2013) examined and analyzed the effect of receiving wire tilting on engendering shadowing is altogether explored for urban situation. Results have presented that changing radio wire tilt significantly affects the shadowing map.…”

Section: Literature Reviewmentioning

confidence: 99%

Radio Propagation Models: A Performance Analysis based Impact of Sparse and Dense Deployment of Nodes under Different Propagation Models in Mobile Ad-hoc Networks (MANETs)

Hussain

Khan

2021

Preprint

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Mobile Ad-hoc Network (MANET) is the most emerging and fast expanding technology since the last two decades. One of the major issue and challenging area in MANET is the process of routing due to dynamic topologies and high mobility of mobile nodes. The exchange of information from source to a destination is known as the process of routing. Spectacular amount of attention has been paid by researchers to reliable routing in ad-hoc networks. Efficiency and accuracy of a protocol depends on many parameters in these networks. In addition to other parameters node velocity and propagation models are among them. Calculating signal strength at receiver is the responsibility of a propagation model while mobility of nodes is responsible for topology of the network. A huge amount of loss in performance is occurred due to variation of signal strength at receiver and obstacles between transmissions. Simulation tools are developed to analyze the weakness and strength of protocols along with different parameters that may impact the performance. The choice of a propagation models have an abundant effect on performance on routing protocols in MANET. In this research, it has been analyzed to check the impact of different propagation models on the performance of Optimized Link State Routing (OLSR) in Sparse and Dense scenarios in MANET. The simulation has been carried out in NS-2 by using performance metrics as average Throughput, average packet drop and average latency. The results predicted that propagation models and mobility has a strong impact on the performance of OLSR in considered scenarios.

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Section: Literature Reviewmentioning

confidence: 99%

Radio Propagation Models: A Performance Analysis based Impact of Sparse and Dense Deployment of Nodes under Different Propagation Models in Mobile Ad-hoc Networks (MANETs)

Hussain

Khan

2021

Preprint

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“…Experimental 158 results on data collected from LTE BSs show that VGC 159 improves the performance of signal strength prediction when 160 compared to traditional models. Similarly, the work in [11] 161 investigates the effect of antenna tilt on radio maps, com-162 paring the path loss models developed by the 3rd generation 163 partnership project (3GPP) [12] for different propagation envi-164 ronments. The results were obtained using a ray tracing tool 165 able to take into account antenna tilts and demonstrate that 166 changing antenna tilt has a significant impact on the shad-167 owing map.…”

Section: A Tilt-dependent Radio Map Prediction 142mentioning

confidence: 99%

Transfer Learning for Tilt-Dependent Radio Map Prediction

Parera

Liao

Malanchini

et al. 2020

IEEE Trans. Cogn. Commun. Netw.

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Machine learning will play a major role in handling 1 the complexity of future mobile wireless networks by improving 2 network management and orchestration capabilities. Due to the 3 large number of parameters that can be monitored and config-4 ured in the network, collecting and processing high volumes of 5 data is often unfeasible or too expensive at network runtime, 6 which calls for taking resource management and service orches-7 tration decisions with only a partial view of the network status. 8 Motivated by this fact, this paper proposes a transfer learning 9 framework for reconstructing the radio map corresponding to a 10 target antenna tilt configuration by transferring the knowledge 11 acquired from another tilt configuration of the same antenna, 12 when no or very limited measurements are available from the 13 target. The performance of the framework is validated against 14 standard machine learning techniques on a data set collected 15 from a 4G commercial base stations. In most of the tested scenar-16 ios, the proposed framework achieves notable prediction accuracy 17 with respect to classical machine learning approaches, with a 18 mean absolute percentage error below 8%. 19 Index Terms-Radio map prediction, antenna tilt, transfer 20 learning. 21 I. INTRODUCTION 22 F IFTH generation wireless networks (5G) are expected to 23 improve the performance of cellular systems, achieving 24 higher data rates, reduced latency, higher reliability and sup-25 port for greater numbers of users. To achieve this, 5G resorts 26 to dense and heterogeneous deployments, coupled with higher 27 flexibility in the network access and core domains, which can 28 be dynamically managed in either a centralized or distributed 29 manner. To cope with such a complex scenario, it is foreseen 30 that machine learning tools will play a major role in enabling 31

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“…The predictive performance are reported to be lower for locations close to the antenna. Similarly, the work in [8] investigates the effect of antenna tilt on radio maps, comparing the path loss models developed by 3GPP for different propagation environments [9] with the results obtained by a ray tracing tool able to take into account antenna tilts. Results demonstrate that changing antenna tilt has a significant impact on the shadowing map, therefore calling for a rethinking of currently available 3GPP propagation models and assumptions, which apply identical shadowing map independently from the antenna tilt.…”

Section: Related Workmentioning

confidence: 99%

Transferring knowledge for tilt-dependent radio map prediction

Parera

Redondi

Cesana

et al. 2018

2018 IEEE Wireless Communications and Networking Conference (WCNC)

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Fifth generation wireless networks (5G) will face key challenges caused by diverse patterns of traffic demands and massive deployment of heterogeneous access points. In order to handle this complexity, machine learning techniques are expected to play a major role. However, due to the large space of parameters related to network optimization, collecting data to train models for all possible network configurations can be prohibitive. In this paper, we analyze the possibility of performing a knowledge transfer, in which a machine learning model trained on a particular network configuration is used to predict a quantity of interest in a new, unknown setting. We focus on the tilt-dependent received signal strength maps as quantities of interest and we analyze two cases where the knowledge acquired for a particular antenna tilt setting is transferred to (i) a different tilt configuration of the same antenna or (ii) a different antenna with the same tilt configuration. Promising results supporting knowledge transfer are obtained through extensive experiments conducted using different machine learning models on a real dataset.

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Impact of Antenna Tilting on Propagation Shadowing Model

Cited by 6 publications

References 6 publications

Radio Propagation Models: A Performance Analysis based Impact of Sparse and Dense Deployment of Nodes under Different Propagation Models in Mobile Ad-hoc Networks (MANETs)

Radio Propagation Models: A Performance Analysis based Impact of Sparse and Dense Deployment of Nodes under Different Propagation Models in Mobile Ad-hoc Networks (MANETs)

Transfer Learning for Tilt-Dependent Radio Map Prediction

Transferring knowledge for tilt-dependent radio map prediction

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