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Chaotic antenna arrays (CAAs) are phased antenna arrays with randomized antenna elements exhibiting unique and spatially dependent phase errors. CAAs are promising for generating strong RF fingerprints that can be used for device authentication. For the RF fingerprint to remain secure, it is crucial that the phase errors remain unknown to the user of the CAA. This on the other hand inhibits conventional beam steering that relies on a known antenna array structure. Additionally, the user with the CAA cannot employ known physical layer security methods that are based on phased antenna arrays. To alleviate this issue, we propose a novel security method in networks with distributed receivers. The approach combines i) distortion caused by changes in the array pattern with ii) encoding based on the phase difference at distributed locations, which makes the method resistant against eavesdropping. Mitigating the distortion and decoding the signal becomes only possible if the eavesdropper can obtain all signals received at all legitimate receivers. 

Laser enhanced direct print additive manufacturing (LE-DPAM) technology has recently been demonstrated to achieve success in packaging of antennas with phase shifters to realize passive phased antenna arrays (PAAs). Utilizing LE-DPAM for PAAs operating in mm-wave bands brings out new challenges that need to be addressed. These challenges are associated with smaller antenna and feature sizes needed for mm-wave band operation, necessity of active circuits for amplification, and number of pads, pad size and pad locations of mm-wave beamformer IC packages. This paper presents our initial progress in scaling LE-DPAM based packaging of PAAs into the mm-wave band operation through consideration and demonstration of discrete components (i.e. antenna array elements and beamformer ICs) that form the PAA structure. Specifically, a stand-alone, passive, 2×2 LE-DPAM based 26 GHz antenna subarray is investigated for its performance. In addition, a 24.5 GHz – 27 GHz beamformer IC is packaged in a stand-alone test article using LE-DPAM and investigated for its mm-wave performance and thermal aspects. 

Chaotic antenna array (CAA)s are phased antenna arrays in which individual elements are randomized in their array position, shape, and feed line length. These randomizations generate spatially dependent large scale phase errors (with respect to antenna elements of a uniform array) that enables distinct physical layer security solutions not available to other wireless systems. Herein, a preliminary study on one such novel method, developed to combat eavesdropping is presented. In the proposed method, the CAA equipped transmitter intentionally distorts its signals based on its own array factor (AF) which includes the phase errors. This distortion significantly hampers demodulation at an eavesdropper, while a legitimate receiver that is aware of the phase errors can compensate for the added distortion. 

A millimeter (mm)-wave beam steering antenna consisting of subarrays of parallel plate lenses is presented for the first time. As compared to a previously reported antenna that utilized subarrays of dielectric slab waveguide lenses, the presented antenna allows to design and control the beamwidth of the radiation pattern in the plane orthogonal to the beam steering plane by stacking the parallel plate lens subarrays. Additionally, full wave simulations of the presented antenna show performance improvements in gain, side lobe level, and field of view in comparison to the previously reported dielectric slab waveguide-based realization. 

Radio frequency (RF) fingerprinting is a hardware-based authentication technique utilizing the distinct distortions in the received signal due to the unique hardware differences in the transmitting device. Existing RF fingerprinting methods only utilize the naturally occurring hardware imperfections during fabrication; hence their authentication accuracy is limited in practical settings even when state-ofthe-art deep learning classifiers are used. In this work, we propose a Chaotic Antenna Array (CAA) system for significantly enhanced RF fingerprints and a deep learning-based device authentication method for CAA. We provide a mathematical model for CAA, explain how it can be cost-effectively manufactured by utilizing mask-free laser-enhanced direct print additive manufacturing (LE-DPAM), and comprehensively analyze the authentication performance of several deep learning classifiers for CAA. Our results show that the enhanced RF signatures of CAA enable highly accurate authentication of hundreds of devices under practical settings.