Sub-6-GHz Energy-Detection-Based Fast On-Chip Analog Spectrum Sensing With Learning-Driven Signal Classification

Cognitive communication utilizes transient openings in the spectrum to communicate opportunistically, which is a promising technique to enable more efficient spectrum usage in an increasingly congested spectrum environment. We aim to address two main challenges associated with cognitive communication: 1) spectrum sensing should be fast and energy efficient for processing a large bandwidth in a short time and 2) the spectrum sensing approach should be able to simultaneously recognize multiple signals that are present. In this article, we propose to address these challenges with a novel design framework that consists of a fast on-chip spectrum sensing in conjunction with a novel learning-based spectrum analysis model at the edge to enhance the optimizations for spectrum agility. We first utilize a model of a programmable analog-based high-quality factor (Q) on-chip spectrum sensor that is capable of scanning the sub-6-GHz band to detect the spectrum usage in less than
$1~\mu $
s. The proposed spectrum sensor also enhances the energy efficiency of the sensing. To complement the on-chip spectrum sensor, a deep learning (DL) model is deployed for a fine-grained signal detection between channels in the 400 MHz to 6-GHz range, which is intended to be executed on edge devices. Simulation results show that the DL model can detect multiple different modulated signals with a mean Intersection-over-Union (IoU) of 86.8% in highly variable bandwidth and center frequency scenarios. Finally, we present a system-level model of our framework to demonstrate the spectrum sensing and classification in the sub-6-GHz frequency band.