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The article examines neural network learning of the sparse array configurations in optimum beamforming. Unlike iterative greedy, convex, and global optimization methods for optimum array design, deep learning enables fast reconfigurations of the sparse array in rapid dynamic propagation environments. We employ three different convolutional neural network architectures with varying simplification and parameter counts. The network is trained to select M out of N uniformly spaced antennas to achieve maximum signal-to-interference and noise ratio (SINR) beamforming. Different values of M are considered, including N = 2 M, for studying network performance under an increased number of subarray classes. We consider one desired source and one interference of arbitrary angle, and delineate the learning results for the two cases where the network is trained with the desired source assuming fixed and varying angles. We discuss the benefits of reducing the number of possible configurations due to sidelobe level reductions. It is also shown that the network performance significantly improves with data augmentations and by removing redundant array configurations which produce the same SINR. 

Vulnerabilities of key based analog obfuscation methodologies that modify the transistor dimensions of a circuit are evaluated. Two attack vectors on a common source amplifier, differential amplifier, operational amplifier, and voltage controlled oscillator are developed. The first attack exploits the lack of possible key combinations permitted around the correct key, which is a result of requiring a unique key to lock the circuit. An average of 5 possible key combinations were returned in an average of 5.47 seconds when executing the key spacing attack. The second attack vector utilizes the monotonic relationship between the sizing of the transistors and the functional response of the circuit to determine the correct key. The average time to execute the attack, while assuming process, voltage, and temperature (PVT) variation of 10%, was 1.18 seconds. Both equal key spacing and non-monotonic key dependencies are discussed as ways to mitigate the threats to future analog obfuscation techniques. 

Our everyday lives are impacted by the widespread adoption of wireless communication systems integral to residential, industrial, and commercial settings. Devices must be secure and reliable to support the emergence of large scale heterogeneous networks. Higher layer encryption techniques such as Wi-Fi Protected Access (WPA/WPA2) are vulnerable to threats, including even the latest WPA3 release. Physical layer security leverages existing components of the physical or PHY layer to provide a low-complexity solution appropriate for wireless devices. This work presents a PHY layer encryption technique based on frequency induction for Orthogonal Frequency Division Multiplexing (OFDM) signals to increase security against eavesdroppers. The secure transceiver consists of a key to frequency shift mapper, encryption module, and modified synchronizer for decryption. The system has been implemented on a Virtex-7 FPGA. The additional hardware overhead incurred on the Virtex-7 for both the transmitter and the receiver is low. Both simulation and hardware evaluation results demonstrate that the proposed system is capable of providing secure communication from an eavesdropper with no decrease in performance as compared with the baseline case of a standard OFDM transceiver. The techniques developed in this paper provide greater security to OFDM-based wireless communication systems.