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Abstract-In this paper, we apply correlation theory methods to obtain a model for the near-carrier oscillator power-spectral density (PSD). Based on the measurement-driven representation of phase noise as a sum of power-law processes, we evaluate closed form expressions for the relevant oscillator autocorrelation functions. These expressions form the basis of an enhanced oscillator spectral model that has a Gaussian PSD at near-carrier frequencies followed by a sequence of power-law regions. New results for the effect of white phase noise, flicker phase noise and random walk frequency modulated phase noise on the near-carrier oscillator PSD are derived. In particular, in the case of 1 phase noise, we show that despite its lack of stationarity it is possible to derive a closed form expression for its effect on an oscillator PSD and show that the oscillator output can be considered to be wide-sense stationary.Index Terms-Correlation theory, frequency noise, Gaussian PSD, Lorentzian PSD, oscillator power-spectral density (PSD), phase noise, power-law process.

Abstract-Physical layer security can provide alternative means for securing the exchange of confidential messages in wireless applications. In this paper, the resilience of wireless multiuser networks to passive (interception of the broadcast channel) and active (interception of the broadcast channel and false feedback) eavesdroppers is investigated. Stochastic characterizations of the secrecy capacity (SC) are obtained in scenarios involving a base station and several destinations. The expected values and variances of the SC along with the probabilities of secrecy outages are evaluated in the following cases: (i) in the presence of passive eavesdroppers without any side information; (ii) in the presence of passive eavesdroppers with side information about the number of eavesdroppers; and (iii) in the presence of a single active eavesdropper with side information about the behavior of the eavesdropper. This investigation demonstrates that substantial secrecy rates are attainable on average in the presence of passive eavesdroppers as long as minimal side information is available. On the other hand, it is further found that active eavesdroppers can potentially compromise such networks unless statistical inference is employed to restrict their ability to attack. Interestingly, in the high signal to noise ratio regime, multiuser networks become insensitive to the activeness or passiveness of the attack.Index Terms-Secrecy capacity, secrecy rate, physical layer security, outage probability, multiuser diversity, multiple eavesdroppers, slow fading and side information.

Several location-based services require accurate location information in indoor environments. Recently, it has been shown that deep neural network (DNN) based received signal strength indicator (RSSI) fingerprints achieve high localization performance with low online complexity. However, such methods require a very large amount of training data, in order to properly design and optimize the DNN model, which makes the data collection very costly. In this paper, we propose generative adversarial networks for RSSI data augmentation which generate fake RSSI data based on a small set of real collected labeled data. The developed model utilizes semi-supervised learning in order to predict the pseudo-labels of the generated RSSIs. A proper selection of the generated data is proposed in order to cover the entire considered indoor environment, and to reduce the data generation error by only selecting the most realistic fake RSSIs. Extensive numerical experiments show that the proposed data augmentation and selection scheme leads to a localization accuracy improvement of 21.69% for simulated data and 15.36% for experimental data.INDEX TERMS Indoor localization, received signal strength indicator (RSSI), deep neural network (DNN), generative adversarial network (GAN), semi-supervised learning.