This paper proposes NeuralWave, an intelligent and non-intrusive user identification system based on human gait biometrics extracted from WiFi signals. In particular, the channel state information (CSI)measurements are first collected from commodity WiFi devices. Then, a collection of data preprocessing schemes are applied to sanitize and calibrate the noisy and erroneous CSI data samples to manifest and augment the gait-induced radio-frequency (RF)signatures. Next, a 23-layer deep convolutional neural network, namely RadioNet, is developed to automatically learn the salient features from the preprocessed CSI data samples. The extracted features constitute a latent representation for the gait biometric that is discriminative enough to distinguish one person from another. Using the latent biometric representation, a softmax multi-class classifier is adopted to achieve accurate user identification. Extensive experiments in a typical indoor environment are conducted to show the effectiveness of our system. In particular, NeuralWave can achieve 87.76 ± 2.14% user identification accuracy for a group of 24 people. To the best of our knowledge, NeuralWave is the first in the literature to exploit deep learning for feature extraction and classification of physiological and behavioral gait biometrics embedded in CSI signals from commodity WiFi.

Providing high-speed communication for mobile users in remote geographic areas or after a disaster occurs is not only critical but also challenging. To counter such challenge, unmanned aerial vehicles (UAVs) have been exploited to provide a fast-deployable and high-speed communication system, where each UAV can serve as an aerial small cell base station to provide WiFi and/or cellular services for the ground users. Despite its fast-deployable and highly maneuverable features, the capacity analysis of aerial small cells is largely missing. To close such gap, a stochastic propagation model for A-to-G aerial channels is first introduced, which takes into account the impact from wave propagation, gaseous absorption, Doppler spread, attitude-dependent shadowing, and multipath fading. Then, by exploiting such model, the area spectral efficiency of the aerial small cells is investigated for both SISO and MIMO cases. Our study reveals the inherent relationship among the area capacity, height and coverage and shows that there exists an optimal attitude that can maximize network capacity and cell coverage.