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Information technology in the field of transmission that is currently developing, one of which is Wi-Fi. Wi-Fi devices provide user convenience in carrying out their activities. The quality of Wi-Fi network performance can be known by the reception of the signal received by the user. If the placement of an access point (AP) is done correctly, the network will be optimized. There are several propagation models in the room that can be used as a guideline in determining the placement of the AP, one slope model is a way to measure the average level of a building and only depends on the distance of the transmitter and receiver. This research was conducted in order to overcome the problem of Wi-Fi network area coverage at the Office of Communication and Information of the City of Palembang. This study conducted an experiment to change the layout of AP's placement, measure and calculate data in priority with the one slope model. The results of measurements and calculations carried out analysis and comparison in order to determine the results of the experiments conducted. The results of this study indicate that an attempt to change the AP layout with one slope model can overcome existing problems and get better Wi-Fi coverage area performance. In the calculation with the one slope model of the 2-trial access point placement results in a decrease and an increase in signal. The signal reduction occurred in experiment 1, whereas in experiment 2 (design 2) the signal increased by 1.46dBm.

Owing to its efficiency in the Internet of Things (IoT) applications in terms of low-power connectivity, IQRF (Intelligent Connectivity using Radio Frequency) technology appears to be one of the most reasonable IoT technologies in the commercial market. To realize emerging smart building applications using IQRF, it is necessary to study the propagation characteristics of IQRF technology in indoor environments. In this study, preliminary propagation measurements are conducted using IQRF transceivers that operate on the 868 MHz band in a peer-to-peer (P2P) configured system. The measurements are conducted both in a single corridor of a building in a Line-of-Sight (LoS) link and two perpendicular corridors in a Non-Line-of-Sight (NLoS) with one single knife-edge link. Moreover, the measured path loss values are compared with the predicted path loss values in order to comparatively assess the prediction accuracy of the well-known empirical models, such as log-distance, ITU, and WINNER II. According to the results, it is concluded that the ITU-1 path loss model agrees well with the measurements and could be used in the planning of an IQRF network deployment in a typical LoS corridor environment. For NLoS corridors, both ITU-3 and WINNERII-2 models could be used due to their higher prediction accuracy. We expect that the initial results achieved in this study could open new perspectives for future research on the development of smart building applications.

In this paper, we present an indoor measurement campaign for TV white space bands inside a university building. The measurement results are compared with different indoor propagation models in the literature. We observed large estimation errors for the total path loss value from all existing models. Consequently, we are proposing a new indoor propagation model for TVWS frequencies, which concatenates the effects of frequency dependent path loss with penetration losses due to walls and windows. Performance comparison with existing models show that the proposed model achieves superior performance compared to existing models in terms of Root-Mean-Squared Error (RMSE).

Formulación de un modelo de propagación que tenga en consideración las principales características de los escenarios indoor. Dicho modelo considera los elementos principales de atenuación, siendo capaz de obtener resultados óptimos. Además, la baja cantidad de recursos necesarios, asegura su fácil uso y empleabilidad, pudiendo servir como una solución de bajo coste computacional para la estimación de la cobertura en escenarios de despliegue de redes LTE en interiores., Development of a propagation model that takes into account the main characteristics of indoor scenarios. This model considers the main elements of attenuation, being able to obtain optimal results. In addition, the low amount of resources, ensures an easy use and good employability and can serve as a low computational cost solution for the estimation of coverage in LTE network deployment in indoor scenarios.

The localization methods based on received signal strength indicator (RSSI) link the RSSI values to the position of the mobile to be located. In the RSSI localization techniques based on propagation models, the accuracy depends on the tuning of the propagation models parameters. In indoor wireless networks, the propagation conditions are hardly predictable due to the dynamic nature of the RSSI, and consequently the parameters of the propagation model may change. In this paper, we present an automatic virtual calibration method of the propagation model that does not require human intervention; therefore, can be periodically performed, following the wireless channel conditions. We also propose a novel RSSI-based localization algorithm that selects the RSSI values according to their strength, and uses a calibrated propagation model to transform these values into distances, in order to estimate the position of the mobile. Copyright © 2011 John Wiley & Sons, Ltd. (We propose an automatic calibration procedure of the signal propagation model that is only based on the RSSIs measured among the anchors and that can be executed periodically and automatically (i.e., without human intervention). Based on the virtual calibration procedure, we propose a localization algorithm that selects the anchors with higher signal strength, and it exploits the calibrated propagation model to relate RSSI measures with distances. Finally, in order to provide the mobile position, the algorithm uses a trilateration method.)

The localization methods based on Received Signal Strength Indicator (RSSI) link the RSSI values to the position of the mobile to be located. In the RSSI localization techniques based on propagation models, the accuracy depends on the tuning of the propagation models parameters. In indoor wireless networks, the propagation conditions are hardly predictable due to the dynamic nature of the RSSI, and consequently the parameters of the propagation model may change. In this paper, we present an automatic virtual calibration method of the propagation model that does not require human intervention; therefore, can be periodically performed, following the wireless channel conditions. We also propose a novel RSSI-based localization algorithm that selects the RSSI values according to their strength, and uses a calibrated propagation model to transform these values into distances, in order to estimate the position of the mobile.

The installation of indoor radio systems requires rather detailed propagation characteristics for any arbitrary configuration, so appropriate wave propagation model must be established. In spite of a number proposed solutions for prediction of the propagation characteristics in WLAN environment, it is difficult to say that we have completely satisfied solution. A neural network propagation model that was trained for particular environment was developed. The network architecture is based on the multilayer perceptron. The neural network results are additionally compared with the numerical results obtained by the deterministic 3-D ray tracing model. The ray tracing model includes three reflected rays from the walls and other obstacles what was enough accurate for the given environment. The neural network is used to absorb the knowledge about given environment through training with three access points. Using such obtained knowledge the network is used to predict signal strength at any spot of space under consideration. The various training algorithms were applied to the network to achieve the best convergence results and best possible network model behavior. The network model was trained by Scaled Conjugate Gradient (SCG), Conjugate Gradient of Fletcher-Reeves (CGF), Quasi-Newton (QN), and Levenberg-Marquardt (LM) algorithms. The comparison of the obtained results is presented., Uvođenje bežičnih komunikacijskih sustava u bilo kakav prostor zahtijeva prilično detaljno poznavanje propagacijskih karakteristika, tako da je potrebno izraditi odgovarajući model rasprostiranja elektromagnetskoga polja. Unatoč većem broju do sada predloženih rješenja za predviđanje propagacijskih karakteristika u bežičnim lokalnim mrežama (WLAN), teško je reći da postoji potpuno zadovoljavajuće rješenje. Razvijen je propagacijski model zasnovan na neuronskoj mreži, koja je obučena za određeni okoliš. Arhitektura je mreže zasnovana na višeslojnom perceptronu. Rezultati dobiveni s pomoću neuronskoga modela uspoređeni su s rezultatima postignutima s determinističkim trodimenzionalnim modelom zasnovanim na metodi slijeđenja zrake. Metoda slijeđenja zrake koristi se trima reflektiranim zrakama od zidova, što osigurava dostatnu točnost za zadani prostor. Neuronska je mreža upotrijebljena za prikupljanje znanja povezanoga s propagacijskim karakteristikama određenog prostora, i to za tri priključne točke. Uporabom tako prikupljenog znanja mreža je upotrijebljena za predviđanje snage signala u bilo kojoj točki razmatranoga prostora. Neuronska je mreža obučavana s nekoliko različitih algoritama kako bi se postigla najbolja konvergencija, to jest model s najboljim karakteristikama. Upotrijebljeni su algoritmi: Konjugirani gradijent (SCG), Konugirani gradijent Fletcher-Reevesa, Quasi_newton (QN) i Leveberg-Marquardt. Prikazani su postignuti usporedni rezultati.

The installation of indoor radio systems requires rather detailed propagation characteristics for any arbitrary configuration, so appropriate wave propagation model must be established. In spite of a number proposed solutions for prediction of the propagation characteristics in WLAN environment, it is difficult to say that we have completely satisfied solution. A neural network propagation model that was trained for particular environment was developed. The network architecture is based on the multilayer perceptron. The neural network results are additionally compared with the numerical results obtained by the deterministic 3-D ray tracing model. The ray tracing model includes three reflected rays from the walls and other obstacles what was enough accurate for the given environment. The neural network is used to absorb the knowledge about given environment through training with three access points. Using such obtained knowledge the network is used to predict signal strength at any spot of space under consideration. The various training algorithms were applied to the network to achieve the best convergence results and best possible network model behavior. The network model was trained by Scaled Conjugate Gradient (SCG), Conjugate Gradient of Fletcher-Reeves (CGF), Quasi-Newton (QN), and Levenberg-Marquardt (LM) algorithms. The comparison of the obtained results is presented.