Investigation of indoor propagation of WLAN signals (original) (raw)
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Development of Radio Wave Propagation Model in Indoor Non-Line-of-Sight (NLOS) Scenario
2015
To improve the performance of an indoor WLAN, it is very important to estimate specific received signal strength based on experimental and predicting data. As the design of building layouts and constructed building materials modernize and are complex, it is difficult to estimate the received signal strength values according to those building structure. For this reason, this research develops a new radio wave propagation model for indoor Non-Line-of-Sight (NLOS) scenarios with the help of TP-LINK router .In order to develop the proposed model, the free space model is modified by considering the influence of corridor conditions on each floor based on ray tracing technique at a carrier frequency range of 2.4GHz. Using this model, indoor received signal strength values can be estimated according to the geometrical plan in modeling of indoor radio wave propagation. The performance comparison of channel capacity is implemented with various frequency ranges using MATLAB programming langua...
Analysis of electromagnetic wave propagation in indoor environments
2012
The wireless networks have been the object of many studies and analyzes of current technology industry, providing not only communication with mobility to end users, but also incorporating new applications. One such technology is known as Wireless Fidelity (Wi-Fi) 802.11. This paper aims to present the behavior of the propagation of electromagnetic waves radiated from an access point Wi-Fi (AP), with different positions of the antenna. These analyses were based on measurements taken in an environment considering line of sight (LOS) at different distances from the AP, but in a confined environment. Consequently, although there were no obstacles (LOS) between transmitter and receiver, there were conditions of confinement on the propagated signal, given the characteristics of this environment (such as the height of the ceiling and the walls themselves). Thus, measurements were made by switching the antenna positioning of the access point, vertically and horizontally. From the analysis of these measurements, it was possible to verify the positioning of the antenna in the access point that generated improved signal coverage, even moderately. In addition, from the measured data (statistically based) the technique of linear regression was used in order to generate mathematical models for each specific situation measured. These proposed models were compared to the Friis model plus correction factors, and were used both for validating the measurements, as a basis for installation of new access points in similar environments.
Characteristics of the indoor propagation channel in 1.9 GHz band
This paper presents results of propagation measurements carried out in the frequency range 1 8 2 0 1 8 2 0 1 8 2 0 GHz inside a building, using network analyser. Wideband properties of the channel, described through mean delay and delay spread, and a narrowband local statistics of the received power have been presented. For each transmitter and receiver antennas location two propagation cases have been considered, line of sight (LOS) and obstructed line of sight (NLOS) -the direct path component was attenuated by radio absorbing mat near the receiver.
Characterization of Indoor Propagation Properties and Performance Evaluation for 2.4Ghz Band Wi-Fi
SSRN Electronic Journal
Indoor wireless systems poses one of the biggest design challenges although it is the most flexible and easily deployable method of implementing Local Area Networks. This difficulty in predicting the propagation of radio frequency wave in indoor environments is caused by reflection, refraction, diffraction and scattering of signals due to closed proximities to furniture, walls, human beings, and reflectors like ceiling, mirrors and glasses. To help improve the user experience and guarantee good quality of service in indoor situations, the research investigated the throughput and attenuation effect on signal with respect to 4, 5, 6 and 9 inches sizes of blocks walls respectively. The characterized Path-loss exponent was 1.999 and differed from the free space model, Wall and Floor Factor model and ITU model by 53.54dB, 6.42dB and 6.85dB respectively.
An Investigation on the Effects of Wall Parameters on the Indoor Wireless Propagations
2007 5th Student Conference on Research and Development, 2007
The type of the construction materials of the interior walls of the indoor environments plays a great role in the propagation of the transmitted signals inside the buildings. A comparison of calculated and simulated Fresnel reflection and transmitted coefficients at 2.4 GHz and 900 MHz for a variety of typical exterior building surfaces has been achieved. The effect of the different types of wall on the path loss prediction had been conducted by using a ray tracing program with real time reflection and refraction phenomena.
2018
This paper presents path loss models based on extensive propagation measurements performed at 2.4 GHz and 5.8 GHz in a modern indoor office layout typical of small and medium-sized businesses, namely: the open-space office. Measurements were conducted using a vector network analyzer which covers frequencies up to 6 GHz, and ultra-wideband omnidirectional vertically-polarized antennas. The data were recorded under the same conditions and with the same antennas for both 2.4 GHz and 5.8 GHz. 940 transmitter-receiver location and height combinations were studied, as well as antenna configurations in both line-of-sight and non-line-of-sight. A second measurement campaign was conducted to quantify the variation amount on the expected power loss in realistic scenarios that include the effect of people movement and showed that the mean path loss further increases by up to 4 dB due to people's presence and movement, with variations up to 9 dB when the activity level is high.
STUDY OF VARIOUS INDOOR PROPAGATION MODELS
Indoor Propagation modeling is demanded for the maintenance of indoors-wireless services. Propagation models provide estimates of signal strength and time dispersion in many indoor environments. These data are valuable in the design and installation of indoor radio systems. We propose improving existing channel models by building partitioning technique. Based on the measurement results the easy-to-use empirical propagation predication models were derived for both of the buildings with satisfactory accuracy. The result used to determine the path loss exponent and standard deviation. It similarly shows that the RSS values Vs distance help in determine the variation in multi-wall model and single wall.
SinkrOn, 2021
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.