Chaotic antenna arrays (CAAs) have been shown to produce enhanced and robust RF fingerprints, enabling more reliable device authentication using machine learning (ML) compared to traditional methods based on subtle hardware imperfections. In this paper, we present a novel CAA-specific multipath channel model to accurately capture the CAA’s phase errors across all propagation paths providing a basis for processing schemes that remove the channel effect, enabling extraction of the RF signature of the CAA. In addition, we provide a comprehensive experimental validation of CAA-based authentication under practical wireless channel conditions and include full details covering the design, fabrication, and characterization of custom CAA nodes, along with their integration within a software-defined radio (SDR) testbed for over-the-air measurements. This manuscript shows, for the first time, that training of the ML-based authenticator on the CAA RF fingerprints can be conducted under line-of-sight (LOS) conditions and effectively generalized to diverse scenarios, including non-line-of-sight (NLOS) environments, as long as a dominant propagation path exists. High authentication accuracy is consistently achieved when this key spatial channel characteristic is preserved. Accuracy exceeds 90% in LOS and reaches up to 87% in NLOS conditions with a single dominant reflected path. This study provides the first practical validation of spatially varying CAA fingerprints, underscoring their promise for secure and robust physical layer authentication across varied wireless conditions.

A traditional array (TA) multiple-input multiple-output (MIMO) architecture in mmWave with hybrid beamforming suffers from high power consumption and hardware overhead. Therefore, a lens antenna subarray (LAS)-MIMO architecture has been recently proposed as a promising technology for a power-efficient system and reducing hardware cost and complexity. Additionally, the LAS-MIMO can offer spectral efficiency (SE) performance close to TA-MIMO and higher than single-lens antenna array (SLA)-MIMO. In this paper, we propose a hybrid precoding algorithm for the LAS-MIMO in mmWave to efficiently control the LAS design. The precoding problem is formulated as a sparse reconstruction problem due to the sparse behavior of mmWave channel. The proposed algorithm is an iterative process developed jointly using artificial bee colony (ABC) optimization with orthogonal matching pursuit (OMP) algorithms. In each iteration, the algorithm first selects the switches for each lens randomly using ABC and then uses OMP to approximate optimal unconstrained precoders. This process continues until achieving maximum SE. The simulation results show that LAS has around a 30% increase in SE compared to SLA while providing a significant gain in energy efficiency (EE) for single radio-frequency (RF) chain and multi RF chain scenarios.

EM side-channel leakage is typically the derivative of the power consumption profile of a circuit. Since the fluctuations of the supply voltage strongly depend on the topology and characteristics of the power distribution network (PDN), the design of the PDN has a direct impact on the EM side-channel leakage signature. In this paper, the security implications of distributed on-chip voltage regulators against EM side-channel attacks are investigated. Extensive HFSS simulations have demonstrated that the maximum EM radiation can be reduced by 33 dB and 11 dB, respectively, at the front and back sides of an integrated circuit with distributed on-chip voltage regulation since the power is delivered locally through partially shorter and thinner metal lines as compared to the designs with offchip voltage regulators.