Reconfigurable and Intelligent Ultrawideband Angular Sensing: Prototype Design and Validation

The emergence of beyond-licensed spectrum sharing in FR1 (0.45-6 GHz) and FR2 (24-52 GHz) along with the multi-antenna narrow-beam-based directional transmissions demand a wideband spectrum sensing in temporal and spatial domains. We referred to it as ultrawideband angular spectrum sensing (UWAS), and it consists of digitization followed by the characterization of the wideband spectrum. In this article, we design and develop a state-of-the-art UWAS prototype using universal software radio peripheral (USRPs) and LabVIEW NXG for validation in the real-radio environment. Since 5G is expected to co-exist with LTE, the transmitter generates the multidirectional multiuser wideband traffic via LTE specific single-carrier frequency division multiple access (SC-FDMA) approach. At the receiver, the first step of wideband spectrum digitization is accomplished using a novel approach of integrating sparse antenna-array with reconfigurable sub-Nyquist sampling (SNS). The reconfigurable SNS allows the digitization of noncontiguous spectrum via low-rate analog-to-digital converters, but it needs intelligence to choose the frequency bands for digitization. We explore a multiplay multi-armed bandit-based learning algorithm to embed intelligence. Compared with previous works, the proposed characterization (frequency band status and direction-of-arrival estimation) approach does not need prior knowledge of received signal distribution. The detailed experimental results for various spectrum statistics, power gains, and antenna array arrangements validate the functional correctness, superiority, and feasibility of the proposed UWAS over state-of-the-art approaches.