In this paper, we introduce an analytical study of the impact of high-power amplifier (HPA) nonlinear distortion (NLD) on the bit error rate (BER) of multicarrier techniques. Two schemes of multicarrier modulations are considered in this work: the classical orthogonal frequency division multiplexing (OFDM) and the filter bank-based multicarrier using offset quadrature amplitude modulation (FBMC/OQAM), including different HPA models. According to Bussgang's theorem, the in-band NLD is modeled as a complex gain in addition to an independent noise term for a Gaussian input signal. The BER performance of OFDM and FBMC/OQAM modulations, transmitting over additive white Gaussian noise (AWGN) and Rayleigh fading channels, is theoretically investigated and compared to simulation results. For simple HPA models, such as the soft envelope limiter, it is easy to compute the BER theoretical expression. However, for other HPA models or for real measured HPA, BER derivation is generally intractable. In this paper, we propose a general method based on a polynomial fitting of the HPA characteristics and we give theoretical expressions for the BER for any HPA model.
Currently, indoor localization is among the most challenging issues related to the Internet of Things (IoT). Most of the state-of-the-art indoor localization solutions require a high computational complexity to achieve a satisfying localization accuracy and do not meet the memory limitations of IoT devices. In this paper, we develop a localization framework that shifts the online prediction complexity to an offline preprocessing step, based on Convolutional Neural Networks (CNN). Motivated by the outstanding performance of such networks in the image classification field, the indoor localization problem is formulated as 3D radio image-based region recognition. It aims to localize a sensor node accurately by determining its location region. 3D radio images are constructed based on Received Signal Strength Indicator (RSSI) fingerprints. The simulation results justify the choice of the different parameters, optimization algorithms, and model architectures used. Considering the trade-off between localization accuracy and computational complexity, our proposed method outperforms other popular approaches.
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