  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 as well as spatial
domains. We referred to it as ultra-wideband angular spectrum sensing (UWAS),
and it consists of digitization followed by characterization of the wideband
spectrum. In this paper, we design and develop state-of-the-art UWAS prototype
using USRPs and LabVIEW NXG for the validation in the real-radio environment.
Since 5G is expected to co-exist with LTE, the transmitter generates the
multi-directional multi-user 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
non-contiguous spectrum via low-rate analog-to-digital converters, but it needs
intelligence to choose the frequency bands for digitization. We explore the
multi-play multi-armed bandit based learning algorithm to embed intelligence.
Compared to 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
along with lower complexity validate the functional correctness, superiority
and feasibility of the proposed UWAS over state-of-the-art approaches.
