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Privacy issues related to video camera feeds have led to a growing need for suitable alternatives that provide functionalities such as user authentication, activity classification and tracking in a noninvasive manner. Existing infrastructure makes Wi-Fi a possible candidate, yet, utilizing traditional signal processing methods to extract information necessary to fully characterize an event by sensing weak ambient Wi-Fi signals is deemed to be challenging. This paper introduces a novel end to-end deep learning framework that simultaneously predicts the identity, activity and the location of a user to create user profiles similar to the information provided through a video camera. The system is fully autonomous and requires zero user intervention unlike systems that require user-initiated initialization, or a user held transmitting device to facilitate the prediction. The system can also predict the trajectory of the user by predicting the location of a user over consecutive time steps. The performance of the system is evaluated through experiments.

This paper proposes a spectrum selection scheme and a transmit power minimization scheme for a device-to-device (D2D) network cross-laid with a cloud radio access network (CRAN). The D2D communications are allowed as an overlay to the CRAN as well as in the unlicensed industrial, scientific and medical radio (ISM) band. A link distance based scheme is proposed and closed-form approximations are derived for the link distance thresholds to select the operating band of the D2D users. Furthermore, analytical expressions are derived to calculate the minimum required transmit power to achieve a guaranteed level of quality of service in each operating band. The results demonstrate that the proposed scheme achieves nearly 50% power saving compared to a monolithic (purely overlay or purely ISM band) D2D network.

A novel content caching strategy is proposed for a cache enabled device-to-device (D2D) network where the user devices are allowed to communicate using millimeter wave (mmWave) D2D links (> 6 GHz) as well as conventional sub 6 GHz cellular links. The proposed content placement strategy maximizes the successful content delivery probability of a line of sight D2D link. Furthermore, a heuristic algorithm is proposed for efficient content delivery. The overall scheme improves the successful traffic offloading gain of the network compared to conventional cache-hit maximizing content placement and delivery strategies. Significant energy efficiency improvements can also be achieved in ultra-dense networks.

The paper considers physical layer security (PLS) of an untrusted unmanned aerial vehicle (UAV) network, where a multitude of UAVs communicate in full-duplex (FD) mode. A source-based jamming (SBJ) scheme is exploited for secure communication without utilizing any external jammers. Firstly, the optimal power allocation between the confidential signal and the jamming signal is derived to maximize the secrecy rate of each link between the source and the destination. Then, the best UAV selection scheme is proposed to maximize the overall secrecy rate of the network. The corresponding secrecy outage probability (SOP) and the average secrecy rate (ASR) of the network are analyzed based on the proposed UAV selection and the optimal power allocation schemes. Asymptotic results are also obtained to derive the achievable diversity order. Results are validated through numerical evaluations while providing useful network design insights such as locations and altitudes of the UAVs.