  With the introduction of shared spectrum sensing and beam-forming based
multi-antenna transceivers, 5G networks demand spectrum sensing to identify
opportunities in time, frequency, and spatial domains. Narrow beam-forming
makes it difficult to have spatial sensing (direction-of-arrival, DoA,
estimation) in a centralized manner, and with the evolution of paradigms such
as artificial intelligence of Things (AIOT), ultra-reliable low latency
communication (URLLC) services and distributed networks, intelligence for edge
devices (Edge-AI) is highly desirable. It helps to reduce the
data-communication overhead compared to cloud-AI-centric networks and is more
secure and free from scalability limitations. However, achieving desired
functional accuracy is a challenge on edge devices such as microcontroller
units (MCU) due to area, memory, and power constraints. In this work, we
propose low complexity neural network-based algorithm for accurate DoA
estimation and its efficient mapping on the off-the-self MCUs. An ad-hoc
graphical-user interface (GUI) is developed to configure the STM32
NUCLEO-H743ZI2 MCU with the proposed algorithm and to validate its
functionality. The performance of the proposed algorithm is analyzed for
different signal-to-noise ratios (SNR), word-length, the number of antennas,
and DoA resolution. In-depth experimental results show that it outperforms the
conventional statistical spatial sensing approach.
