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Millimeter-wave (mmWave) systems require a large number of antennas, which makes the beam training challenging and time-consuming for conventional phased arrays. Recently, a true-time-delay (TTD) array-based beam training algorithm has been shown as an effective solution to overcome the training overhead in large arrays. In this paper, we present a custombuilt over-the-air (OTA) testbed to study the effects of hardware impairments on the TTD-based beam training and verify its feasibility in a real system. We proposed an orthogonal matching pursuit (OMP) based reconstruction algorithm along with a phase calibration dictionary to combat nonidealities such as strong frequency selectivity and phase misalignment in the received raw IQ signal. Post-processing results showed that with the nonideality effects properly handled, the 3D TTD beam training algorithm can achieve high AOA estimation accuracy. 

In this paper, we present a method for decoding uplink messages in Internet of Things (IoT) networks that employ packet repetition. We focus on the Sigfox protocol, but our approach is applicable to other IoT protocols that employ message repetition. Our approach endeavors to enhance the reliability of message capture as well as the error rate performance at the base station. To achieve this goal, we propose a novel technique that capitalizes on the unique features of the IoT network’s uplink transmission structure. Through simulations, we demonstrate the effectiveness of our method in various scenarios, including single-user and multi-user setups. We establish the resilience of our approach under higher system loads and interference conditions, showcasing its potential to improve IoT network performance and reliability even when a large number of devices operates over limited spectrum. Our findings reveal the potential of the proposed method as a promising solution for enabling more dependable and energy-efficient communication in IoT Low Power Wide Area Networks.