# Intelligent Wide-band Spectrum Classifier

## TL;DR

This paper presents a supervised learning-based method for classifying narrow-band signals within wide-band spectra, utilizing compressed sensing to reduce sampling rates and a random forest classifier for modulation identification, demonstrating superior performance.

## Contribution

It introduces a novel approach combining compressed sensing and machine learning for efficient wide-band spectrum classification of narrow-band signals.

## Key findings

- Superior classification accuracy compared to recent algorithms
- Effective spectrum acquisition at reduced sampling rates
- Robust performance across various empirical setups

## Abstract

We introduce a new technique for narrow-band (NB) signal classification in sparsely populated wide-band (WB) spectrum using supervised learning approach. For WB spectrum acquisition, Nyquist rate sampling is required at the receiver's analog-to-digital converter (ADC), hence we use compressed sensing (CS) theory to alleviate such high rate sampling requirement at the receiver ADC. From the estimated WB spectrum, we then extract various spectral features of each of the NB signal. These features are then used to train and classify each NB signal into its respective modulation using the random forest classifier. In the end, we evaluate the performance of the proposed algorithm under different empirical setups and verify its superior performance in comparison to a recently proposed signal classification algorithm.