scholarly journals INVESTIGATION ON NEED FOR SPECIFIC PROPAGATION MODEL FOR SPECIFIC ENVIRONMENT BASED ON DIFFERENT TERRAIN CHARACTERISTICS

2018 ◽  
Vol 19 (2) ◽  
pp. 90-104
Author(s):  
Jide Julius Popoola ◽  
Akinlolu Adediran Ponnle ◽  
Yekeen Olajide Olasoji ◽  
Samson Adenle Oyetunji

ABSTRACT: Owing to their speed of excution as well as their limited reliance on detailed knowledge of the terrain characteristics of the service environments, empirical propagation models have enjoyed general acceptability in the wireless communication research community. However, recent industrial observations show that no single propagation model can best fit all the radio service environments, which led to the hypothesis of specific models for specific environments. In order to scientifically verify this hypothesis, the study presented in this paper investigated the performance of the free space propagation loss (FSPL) model in two different radio environments characterised with different types of obstructions. The investigation was conducted through field strength distribution measurement of two broadcasting radio stations transmitting at 96.5 MHz and 102.3 MHz. The field strength measurement data obtained were analysed. The result of the analysis shows gross disparity between the measured path losses and calculated path losses using FSPL model. The disparity thus necessitates the modification of the FSPL model in order to develop each propagation model for each of the two radio stations employed and their environment. The developed models were then evaluated to ascertain their performances relative to the FSPL model. The performance evaluation results show that the predictions of the developed propagation models vary for each of the two environments. Furthermore, the comparative performance evaluation result of the developed models with similar studies in the literature shows that the developed models perform favourably. The overall result from the developed models confirms the hypothesis that each location requires a specific propagation model for proper radio wave design and quality of signal transmission and reception. ABSTRAK: Kelebihan yang ada pada kelajuan perlaksanaannya dan juga kurang pergantungannya pada butiran terperinci ciri-ciri khusus bentuk rupa bumi di persekitaran servisnya, model penyebaran empirik telah diterima umum dalam komuniti kajian komunikasi tanpa wayar. Walau bagaimanapun, pemerhatian industri terkini menunjukkan tidak ada sebarang model penyebaran yang sesuai bagi semua keadaan servis radio, ini menghala kepada hipotesis keperluan model tertentu pada keadaan servis tertentu. Bagi menentusahkan secara saintifik hipotesis ini, kajian yang dibentangkan dalam kertas ini mengkaji tentang prestasi model kehilangan penyebaran pada ruang bebas (FSPL) dalam dua persekitaran radio berlainan melalui beberapa jenis halangan berbeza. Kajian telah dijalankan ke atas dua stesen radio penyiaran pada frekuensi 96.5 MHz dan 102.3 MHz melalui ukuran sebaran ruang keupayaan. Data ukuran ruang keupayaan telah diperoleh dan dianalisa. Keputusan analisis menunjukkan keputusan tidak seragam yang melampau antara ukuran kehilangan laluan dan pada kiraan model FSPL. Ketidaksamaan ini memungkinkan keperluan mengubah model FSPL bagi membangunkan model penyebaran pada setiap dua radio stesen yang digunakan dan persekitarannya. Model yang dibangunkan ini kemudiannya dinilai bagi mengesahkan prestasinya dengan model FSPL. Keputusan penilaian menunjukkan perbezaan pada jangkaan model penyebaran bagi setiap dua keadaan. Tambahan, keputusan perbandingan model yang dibangunkan dalam karya ini adalah serupa seperti kajian lain yang berkaitan. Secara keseluruhannya model yang dibangunkan ini mengesahkan hipotesis bahawa setiap lokasi memerlukan model penyebaran bagi rekaan gelombang radio yang sesuai dan juga kualiti signal penyebaran dan penerimaan.

2021 ◽  
Author(s):  
Stefanos Sotirios Bakirtzis ◽  
Jiming Chen ◽  
Kehai Qiu ◽  
Jie Zhang ◽  
Ian Wassell

Efficient and realistic indoor radio propagation modelling tools are inextricably intertwined with the design and operation of next generation wireless networks. Machine learning (ML)-based radio propagation models can be trained with simulated or real-world data to provide accurate estimates of the wireless channel characteristics in a computationally efficient way. However, most of the existing research on ML-based propagation models focuses on outdoor propagation modelling, while indoor data-driven propagation models remain site-specific with limited scalability. In this paper we present an efficient and credible ML-based radio propagation modelling framework for indoor environments. Specifically, we demonstrate how a convolutional encoder-decoder can be trained to replicate the results of a ray-tracer, by encoding physics-based information of an indoor environment, such as the permittivity of the walls, and decode it as the path-loss (PL) heatmap for an environment of interest. Our model is trained over multiple indoor geometries and frequency bands, and it can eventually predict the PL for unknown indoor geometries and frequency bands within a few milliseconds. Additionally, we illustrate how the concept of transfer learning can be leveraged to calibrate our model by adjusting its pre-estimate weights, allowing it to make predictions that are consistent with measurement data. <br>


2021 ◽  
Author(s):  
Stefanos Sotirios Bakirtzis ◽  
Jiming Chen ◽  
Kehai Qiu ◽  
Jie Zhang ◽  
Ian Wassell

Efficient and realistic indoor radio propagation modelling tools are inextricably intertwined with the design and operation of next generation wireless networks. Machine learning (ML)-based radio propagation models can be trained with simulated or real-world data to provide accurate estimates of the wireless channel characteristics in a computationally efficient way. However, most of the existing research on ML-based propagation models focuses on outdoor propagation modelling, while indoor data-driven propagation models remain site-specific with limited scalability. In this paper we present an efficient and credible ML-based radio propagation modelling framework for indoor environments. Specifically, we demonstrate how a convolutional encoder-decoder can be trained to replicate the results of a ray-tracer, by encoding physics-based information of an indoor environment, such as the permittivity of the walls, and decode it as the path-loss (PL) heatmap for an environment of interest. Our model is trained over multiple indoor geometries and frequency bands, and it can eventually predict the PL for unknown indoor geometries and frequency bands within a few milliseconds. Additionally, we illustrate how the concept of transfer learning can be leveraged to calibrate our model by adjusting its pre-estimate weights, allowing it to make predictions that are consistent with measurement data. <br>


Author(s):  
Preeti Saini ◽  
Rishi Pal Singh ◽  
Adwitiya Sinha

Background: Acoustic waves have a large range of applications in UWSNs from underwater monitoring to disaster management, military surveillance to assisted navigation. Acoustic waves are primarily used for wireless communication in water. But radio waves are more suitable than acoustic waves for many underwater applications (e.g. real-time applications, shallow water applications). Objectives: A propagation model is required to effectively design a radio wave based UWSN. Propagation model predicts the average received signal strength at a given distance from the transmitter and the variability of the signal strength in close spatial proximity to a particular location. Various radio propagation models are developed for air. Methods: The performance of RF-EM waves underwater is not the same as that in the air. Many parameters which have real-value in the air becomes complex valued in seawater. Thus, propagation models for air cannot be directly used to calculate propagation loss underwater. Various radio propagation models are developed for water by Al-Shamaa’a et al., Uribe and Grote, Jiang et al., Elrashidi et al., Hattab et al. Each model has some merits and demerits. Path loss model developed by Al-Shamma’a et al. is a simple model based on attenuation only. Results: Uribe and Grote have introduced distance-dependent attenuation coefficient in path loss calculation. Path loss model by Jiang et al. calculates path loss for freshwater. Model by Hattab et al. is specifically designed for UWSN. According to the authors, it is the first path loss model developed for UWSN. Elrashidi et al. have calculated path loss for freshwater and seawater at 2.4 GHz. The model includes the effect of the reflected signals on the received signal by the receiver node. Conclusion: The paper presents a comparative analysis of these various radio propagation models developed for underwater. Among these models, the radio propagation model by Hattab et al. is more realistic and covers both propagation loss and interface loss. According to the authors, it is the first radio propagation model developed for UWSNs.


SinkrOn ◽  
2021 ◽  
Vol 5 (2) ◽  
pp. 239-250
Author(s):  
Panangian Mahadi Sihombing ◽  
Maksum Pinem ◽  
Sri Indah Rezkika

Wireless internet service in educational buildings plays a crucial role in telecommunications for the knowledge sharing process. Therefore, various factors that may limit internet services coverage in the building should be eliminated or reduced. One such factor is path losses. Path losses are caused by multiple obstacles between the transmitting and receiving antennas. The problem of path losses in the education building can be solved by providing signal booster devices or Wireless Fidelity (Wi-Fi). But not all college buildings have such tools. Besides, WiFi devices also have limitations on bandwidth and the number of users. Thus, mobile communication devices or smartphones located inside the education buildings still need internet service coverage from the transmitter antenna outside the building. An accurate propagation model is required so that the transmitter antenna outside the building can provide internet service coverage to the inside of the building. This paper had been analyzed the selection of propagation models using three validation formulas, namely Mean Error (ME), Root Mean Square Error (RMSE), and Standard Deviation Error (SDE). This paper used several propagation models, namely the 3GPP Model, Winner+ Model, and COST231 Model. Based on the analysis of calculation and measurement data, it is known that the COST231 model is the most accurate because it has the lowest ME, RMSE, and SDE values.


2021 ◽  
Author(s):  
ALTAF HUSSAIN

Abstract Mobile Ad-hoc NETwork (MANET) is a decentralized type of wireless network. The network is ad hoc because it does not rely on a pre-existing infrastructure, such as routers in wired networks or access points in managed wireless networks. 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 efficiency and accuracy of a protocol depend on many parameters in these networks. In addition to other parameters node velocity and propagation models are among them. Calculating signal strength at the receiver is the responsibility of a propagation model while the mobility of nodes is responsible for the topology of the network. A huge amount of loss in performance is occurred due to the variation of signal strength at the receiver while increasing and decreasing the distance of nodes. In this paper, a novel approach is identified and portrayed of the said propagation models based on distance from each other. It has been analyzed to check distance based performance evaluation of Two Ray Ground and Free Space Radio Wave Propagation Models on the performance of Ad-hoc On-demand Distance Vector (AODV) Routing Protocol in MANET. The simulation has been carried out in (Network Simulator-2) NS-2 by using performance metrics that are Average Throughput (kpbs), Average Latency (milliseconds) and Average Packet Drop (packets). The results predicted that propagation models and mobility have a strong influence on the performance of AODV in considered distance based scenario.


2021 ◽  
Author(s):  
Altaf Hussain ◽  
Muhammad Rafiq Khan

Abstract 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.


2007 ◽  
Vol 5 ◽  
pp. 367-372 ◽  
Author(s):  
M. Neuland ◽  
T. Kürner

Abstract. Propagation models are very important for the development and deployment of wireless communication networks. They are able to predict the path loss for different propagation conditions, but cannot include all propagation phenomena in detail. This fact leads to variations between predicted and measured field strengths. These variations can be reduced by calibrating some parameters of the propagation models with the help of exact measurement data. However, two problems occur when applying measurement data. On the one hand, the maps used for the prediction have only a limited resolution. On the other hand, the GPS data are erroneous due to the limited GPS accuracy and due to sampling errors. These errors can lead to variations up to 200 m between the measured positions and the possible positions on the road network. Therefore, a map-matching algorithm has to be applied which projects the wrong GPS positions automatically onto the street vectors used for the predictions. Thus, a good basis of data for calibration can be created.


Author(s):  
Zichen Wang ◽  
Jian Xu ◽  
Xuefeng Zhang ◽  
Can Lu ◽  
Kangkang Jin ◽  
...  

AbstractThis paper proposes a two-dimensional underwater sound propagation model using the Discontinuous Galerkin Finite Element Method (DG-FEM) to investigate the influence of current on sound propagation. The acoustic field is calculated by the convected wave equation with the current speed parameter. Based on the current speed data from an assimilation model, a two-dimensional coupled acoustic propagation model in the Fram Strait water area is established to observe the variability in propagation loss under different seasonal velocities in the real ocean environment. The transmission loss and signal time structure are examined. The results obtained in different source frequencies are also compared. It appears that the current velocity, time and range variation all have an effect on underwater sound propagation.


Author(s):  
М.Б. ПРОЦЕНКО ◽  
В.В. ГРОМОЗДИН ◽  
М.С. КОЗУБ

Сформулирована и детализирована методика оценивания граничной дальности береговых ОВЧ радиостанций в направлении Берег-Судно, которая основана на зависимостях напряженности поля, полученных эмпирическим путем. Определены численные значения граничной дальности ОВЧ радиосвязи применительно к типовому судовому радиооборудованию и шумовой обстановке вблизи судовой антенны. Проведена оценка максимальных допусков определения граничной дальности ОВЧ радиосвязи. The procedure for estimating the boundary distance of the shore VHF radio stations in the shore-to-ship direction, which is based on the dependences of the electromagnetic field strength obtained empirically, has been formulated and detailed. Numerical values of the boundary distance of VHF radio communication in relation to typical ship radio equipment and the noise environment near the ship's antenna are determined. The estimation of the maximum tolerances for determining the boundary distance of VHF radio communication is carried out.


2019 ◽  
Vol 2019 ◽  
pp. 1-6 ◽  
Author(s):  
Sun-Kuk Noh ◽  
DongYou Choi

Rapidly rising demand for radio communication and the explosion in the number of mobile communications service subscribers have led to the need for optimization in the development of fifth-generation (5G) mobile communication systems. Previous studies on the development of propagation models considering a propagation environment in the existing microwave band have been mainly focused on analyzing the propagation characteristics with regard to large-scale factors such as path losses, delay propagation, and angle diffusions. In this paper, we investigated the concept of spatial and time changes ratios in the measurement of wave propagations and measured RSRP of Long Term Evolution (LTE) signals at three locations considering the time rate of 1% and 50%. We confirmed the concept of spatial and time changes rate based on the results of analyzing the signal data measured and proposed the propagation models 1 and 2 in microcell downtown. The forecast results using proposed models 1 and 2 were better than the COST231 model in both indoor and outdoor measured places. It was predicted between a time rate of 1% and 50% indoor within 400m and outdoor within 200m. In the future, we will study the propagation model of 5G mobile communication as well as the current 4G communication using artificial intelligence technology.


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