This article explores the use of ambient radio frequency (RF) signals for human presence detection through deep learning. Using Wi-Fi signal as an example, we demonstrate that the channel state information (CSI) obtained at the receiver contains rich information about the propagation environment. Through judicious preprocessing of the estimated CSI followed by deep learning, reliable presence detection can be achieved. Several challenges in passive RF sensing are addressed. With presence detection, how to collect training data with human presence can have a significant impact on the performance. This is in contrast to activity detection when a specific motion pattern is of interest. A second challenge is that RF signals are complex-valued. Handling complex-valued input in deep learning requires careful data representation and network architecture design. Finally, human presence affects CSI variation along multiple dimensions; such variation, however, is often masked by system impediments, such as timing or frequency offset. Addressing these challenges, the proposed learning system uses preprocessing to preserve human motion-induced channel variation while insulating against other impairments. A convolutional neural network (CNN) properly trained with both magnitude and phase information is then designed to achieve reliable presence detection. Extensive experiments are conducted. Using off-the-shelf Wi-Fi devices, the proposed deep-learning-based RF sensing achieves near-perfect presence detection during multiple extended periods of test and exhibits superior performance compared with leading edge passive infrared sensors. A comparison with existing RF-based human presence detection also demonstrates its robustness in performance, especially when deployed in a completely new environment. The learning-based passive RF sensing thus provides a viable and promising alternative for presence or occupancy detection.

Universal hypothesis testing (UHT) refers to the problem of deciding whether samples come from a nominal distribution or an unknown distribution that is different from the nominal distribution. Hoeffding's test, whose test statistic is equivalent to the empirical Kullback-Leibler divergence (KL divergence), is known to be asymptotically optimal for distributions defined on finite alphabets. With continuous observations, however, the discontinuity of the KL divergence in the distribution functions results in significant complications for UHT. This paper introduces a robust version of the classical KL divergence, defined as the KL divergence from a distribution to the Lévy ball of a known distribution. This robust KL divergence is shown to be continuous in the underlying distribution function with respect to the weak convergence. The continuity property enables the development of an asymptotically optimal test for the university hypothesis testing problem with continuous observations. The optimality is in the same sense as that of the Hoeffding's test and stronger than that of Zeitouni and Gutman. Perhaps more importantly, the developed test statistic can be computed through convex programs, making it much more meaningful in practice. Numerical experiments are also conducted to evaluate its performance as compared with some kernel based goodness of fit test that has been proposed recently.

We consider the problem of secure communication over a (not necessarily degraded) wiretap channel with two-sided channel state information available at the transmitter and the main receiver, respectively. Using the time sharing argument [1], we establish an achievable rate equivocation region for this channel model. The obtained achievable region generalizes several existing results in the literature. More importantly, through examining various cases with different knowledge of channel state information, we provide a systematic study of the impact of channel state information on secure communication.

We characterize the asymptotic performance of nonparametric one- and two-sample testing. The exponential decay rate or error exponent of the type-II error probability is used as the asymptotic performance metric, and an optimal test achieves the maximum rate subject to a constant level constraint on the type-I error probability. With Sanov's theorem, we derive a sufficient condition for one-sample tests to achieve the optimal error exponent in the universal setting, i.e., for any distribution defining the alternative hypothesis. We then show that two classes of Maximum Mean Discrepancy (MMD) based tests attain the optimal type-II error exponent on \mathbb R ^d , while the quadratic-time Kernel Stein Discrepancy (KSD) based tests achieve this optimality with an asymptotic level constraint. For general two-sample testing, however, Sanov's theorem is insufficient to obtain a similar sufficient condition. We proceed to establish an extended version of Sanov's theorem and derive an exact error exponent for the quadratic-time MMD based two-sample tests. The obtained error exponent is further shown to be optimal among all two-sample tests satisfying a given level constraint. Our work hence provides an achievability result for optimal nonparametric one- and two-sample testing in the universal setting. Application to off-line change detection and related issues are also discussed.

The purpose of this monograph is to introduce up-to-date capacity theorems for the two-user Gaussian interference channel, including both single-antenna and multiple-antenna cases. The monograph starts with the single antenna case and introduces the Han and Kobayashi achievable rate region and its various subregions. Several capacity outer bounds are then presented; these outer bounds, together with the achievable rate region, yield several capacity results for the single-antenna Gaussian interference channel. They include the capacity region for strong interference and the sum-rate capacity for Z interference, noisy interference, mixed interference, and degraded interference. For the more complex multiple-antenna case, the interference state is no longer determined solely by the interference strength, as is the case for the single-antenna Gaussian interference channel. Instead, the structure of the interference in different multi-dimensional subspaces plays an equally important role. As a result of this multiple-dimensional signaling, new interference states, including generally strong, generally noisy, and generally mixed interference, are introduced to obtain capacity theorems that generalize those for the single-antenna case.

We study in this paper optimal signaling for downlink transmission with mutual interference. Restricting each receiver to a single user detector, we show that computing the achievable rate region amounts to solving a family of non- convex optimization problems. Recognizing the intrinsic connection between the signal power at the intended receiver and the interference power at the unintended receiver, we convert the original family of non-convex optimization problems into a new family of convex optimization. Closed-form solutions to this new family of optimization problems are derived. Interestingly, with multiple-antenna transmitters and single-antenna receivers, the optimal downlink transmission with interference always reduces to a simple beamforming scheme. Finally, we note that similar procedure can be devised for systems with more than two pairs of mutually interfering users.