Applying Deep Neural Network (DNN) for Robust Indoor Localization in Multi-Building Environment

In the Internet of Things (IoT) era, indoor localization plays a vital role in academia and industry. Wi-Fi is a promising scheme for indoor localization as it is easy and free of charge, even for private networks. However, Wi-Fi has signal fluctuation problems because of dynamic changes of environments and shadowing effects. In this paper, we propose to use a deep neural network (DNN) to achieve accurate localization in Wi-Fi environments. In the localization process, we primarily construct a database having all reachable received signal strengths (RSSs), and basic service set identifiers (BSSIDs). Secondly, we fill the missed RSS values using regression, and then apply linear discriminant analysis (LDA) to reduce features. Thirdly, the 5-BSSIDs having the strongest RSS values are appended with reduced RSS vector. Finally, a DNN is applied for localizing Wi-Fi users. The proposed system is evaluated in the classification and regression schemes using the python programming language. The results show that 99.15% of the localization accuracy is correctly classified. Moreover, the coordinate-based localization provides 50%, 75%, and 93.10% accuracies for errors less than 0.50 m, 0.75 m, and 0.90 m respectively. The proposed method is compared with other algorithms, and our method provides motivated results. The simulation results also show that the proposed method can robustly localize Wi-Fi users in hierarchical and complex wireless environments

Detection and measurement of radio frequency feedback for an on-frequency repeater

International audienceThe radio frequency feedback (RFF) occurs when the insulation is insufficient between the antennas of an on-frequency repeater, increasing digital transmission errors. In addition, a strong RFF could compromise system stability of the on-frequency repeater because of the growing power in the closed-loop. Automatic gain control is widely used by the on-frequency repeater to regulate the power, this solution being generally used with echo cancellation processes. Most of echo cancellation techniques are based on digital processing such as adaptive filters whose the effectiveness and the algorithm speed are depending on the signal frequency, the bandwidth and the closed-loop parameters. This paper describes a solution of RFF estimation and detection regardless of the receiving signal modulation. By using the frequency scanning and the analysis of the power spectral density peaks in the system, this solution is reliable whatever are the values of the gain-margin and the loop-delay. Simulations and experimental implementation using field-programmable gate array validate the solution. In addition, an example of applications is given in the context of the interference cancellation