Indoor Propagation Modeling at 2.4 GHz for IEEE 802.11 Networks

Indoor use of wireless systems poses one of the biggest design challenges. It is difficult to predict the propagation of a radio frequency wave in an indoor environment. To assist in deploying the above systems, characterization of the indoor radio propagation channel is essential. The contributions of this work are two-folds. First, in order to build a model, extensive field strength measurements are carried out inside two different buildings. Then, path loss exponents from log-distance path loss model and standard deviations from log-normal shadowing, which statistically describe the path loss models for a different transmitter receiver separations and scenarios, are determined. The purpose of this study is to characterize the indoor channel for 802.11 wireless local area networks at 2.4 GHz frequency. This thesis presents a channel model based on measurements conducted in commonly found scenarios in buildings. These scenarios include closed corridor, open corridor, classroom, and computer lab. Path loss equations are determined using log-distance path loss model and log-normal shadowing. The chi-square test statistic values for each access point are calculated to prove that the observed fading is a normal distribution at 5% significance level. Finally, the propagation models from the two buildings are compared to validate the generated equations

Indoor Propagation Modeling at 2.4 GHZ for IEEE 802.11 Networks

This paper discusses indoor propagation modeling

Indoor Propagation Channel Models For Wireless Lan Based On 802.11b Standards At 2.4 Ghz Ism Band

The WLAN is a preferred choice of technology for internet connection in the building environment. The indoor models, reported in the literature are mostly studied in the 900 MHz band of cellular standard and quite scarce in the 2.4 GHz frequency band of WLAN 802.11 standard. The frequency band is also dedicated for the WiMAX technology in which deployment in the office environment is essential. In this thesis, the semi-empirical indoor Multi Wall Classic Extended (MWCE) channel model is proposed. The model is compared and evaluated with the empirical OS and other semi-empirical Multi Wall models obtained from the literature; the Multi Wall Classic (MWC) and Multi Wall Linear (MWL). The models are evaluated based on the accuracy of prediction at two floors of office environment in one of the telecommunication company building. The validity of the proposed model is evaluated through comparison with different models of similar type from the literature. The optimized model coefficients for all models, particularly for the wood/glass and brick/concrete the common wall obstacles in the building, are found. The behavior and characterization of all the models studied are investigated by evaluating the variation of the prediction error at several locations of the same propagation condition. The prediction from the MWCE model is significantly improved compared to the OS model. The MWCE model is also observed to have a high and consistent accuracy prediction, comparable with the MWC and MWL models. The accuracy of the MWCE model is also shown to compare closely with different models of similar type from the literature. With simple formulation without invoking too many details and high consistent accuracy prediction, the proposed MWCE model is suitable for prediction of WLAN signal in the indoor environment to be incorporated in the software planning tool

Location-aware computing: a neural network model for determining location in wireless LANs

The strengths of the RF signals arriving from more access points in a wireless LANs are related to the position of the mobile terminal and can be used to derive the location of the user. In a heterogeneous environment, e.g. inside a building or in a variegated urban geometry, the received power is a very complex function of the distance, the geometry, the materials. The complexity of the inverse problem (to derive the position from the signals) and the lack of complete information, motivate to consider flexible models based on a network of functions (neural networks). Specifying the value of the free parameters of the model requires a supervised learning strategy that starts from a set of labeled examples to construct a model that will then generalize in an appropriate manner when confronted with new data, not present in the training set. The advantage of the method is that it does not require ad-hoc infrastructure in addition to the wireless LAN, while the flexible modeling and learning capabilities of neural networks achieve lower errors in determining the position, are amenable to incremental improvements, and do not require the detailed knowledge of the access point locations and of the building characteristics. A user needs only a map of the working space and a small number of identified locations to train a system, as evidenced by the experimental results presented