Featured Application: This work can be applied to track mobile users, manage indoor navigations, provide alarms in secured areas, such as unacceptable hospital areas, military systems and mass rapid transit (MRT) inside enclosed areas. In general, this work is applicable to inside enclosed areas where the specific location is mandatory.Abstract: 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.

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SUMMARYOrthogonal Frequency Division Multiplexing (OFDM) systems have become the most promising wireless communication systems in the recent years. For OFDM systems, there is one major drawback, which is the high peak-to-average power ratio (PAPR). Companding techniques have been frequently proposed to reduce PAPR. Exponential companding technique offers efficient PAPR reduction with a low bit error rate (BER). However, the exponential companding technique is difficult to implement. This paper utilizes the Padé approximation to simplify the exponential companding technique. The simulation results demonstrate that the proposed companding technique offers the same performance results as those of the exponential companding technique, while Additive White Gaussian Noise (AWGN) or multipath fading channel is considered. Further, the hardware implementation results show that the complexity of the proposed companding technique is less than that of the exponential companding technique.

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