The paradigm of wireless communications is shifting from data transfer to ubiquitous services. Perception of the surrounding environment has recently been highlighted as one of the key technologies enabling new applications, such as autonomous driving, extended reality, and healthcare. This movement gives a rise to an emerging concept of integrated sensing and communication (ISAC), where the distributed communication nodes, such as the base station and user devices, serve as probes for wide-area sensing, and the wideband radio signals are shared between communication and sensing. In recent years, a lot of effort has been directed towards the two main pillars of ISAC, i.e., self-localization of a node (positioning) and detection of device-free objects around the node (sensing). Nevertheless, only few of them have been implemented and brought up for real-world verification. This paper provides our latest efforts on validating the feasibility of wireless positioning and sensing. Through over-the-air measurement and processing in a 5G NR millimeter network with a commercial grade user equipment (UE), we demonstrate that the NR positioning and sensing techniques are mature enough to drive the evolution toward perceptive and cognitive wireless communications.

Novel transmission schemes, enabled by recent advances in the fields of metamaterial (MTM), leaky-wave antenna (LWA) and directional modulation (DM), are proposed for enhancing the physical layer (PHY) security. MTM-LWAs, which offer compact, integrated, and cost-effective alternatives to the classic phased-array architectures, are particularly of interest for emerging wireless communication systems including Internet-of-Things. The proposed secure schemes are devised to accomplish the functionalities of directional modulation (DM) transmitters for orthogonal frequency-division multiplexing (OFDM) and non-contiguous OFDM transmissions, while enjoying the implementation benefits of MTM-LWAs. Specifically, transmitter architectures based on the idea of time-modulated MTM-LWA have been put forth as a promising solution for PHY security for the first time. The PHY security for the proposed schemes are investigated from the point of view of both passive and active attacks where an adversary aims to decode secret information and feed spurious data to the legitimate receiver, respectively. Numerical simulations reveal that even when the adversary employs sophisticated state-of-the-art deep learning based attacks, the proposed transmission schemes are resistant to these attacks and reliably guarantee system security.

We study application of parallel and serially concatenated convolutional codes known as turbo codes to the randomized encoding scheme introduced by Wyner for physical layer security. For this purpose, we first study how randomized convolutional codes can be constructed. Then, we use them as building blocks for developing randomized turbo codes. We also develop iterative low-complexity decoders corresponding to the randomized schemes introduced and evaluate the code performance. We demonstrate via several examples that the newly designed schemes can outperform other existing coding methods in the literature (e.g., punctured low density parity check (LDPC) and scrambled BCH codes) in terms of the resulting security gap.

We study application of convolutional codes to the randomized encoding scheme introduced by Wyner as a way of confusing the eavesdropper over a wiretap channel. We describe optimal and practical sub-optimal decoders for the main and the eavesdropper's channels, and estimate the security gap, which is used as the main metric. The sub-optimal decoder works based on the trellis of the code generated by a convolutional code and its dual, where one encodes the data bits and the other encodes the random bits. By developing a code design metric, we describe how these two generators should be selected for optimal performance over a Gaussian wiretap channel. We also propose application of serially concatenated convolutional codes to this setup so as to reduce the resulting security gaps. Furthermore, we provide an analytical characterization of the system performance by extending existing lower and upper bounds for coded systems to the current randomized convolutional coding scenario. We illustrate our findings via extensive simulations and numerical examples, which show that the newly proposed coding scheme can outperform the other existing methods in the literature in terms of security gap.