# Spectral Attention-Driven Intelligent Target Signal Identification on a Wideband Spectrum

## TL;DR

This paper introduces a spectral attention-driven reinforcement learning method for efficient detection of important signals in wideband spectrum, leveraging spectral correlation and adaptive spectrum selection to improve accuracy and efficiency.

## Contribution

It proposes a novel spectral attention-driven reinforcement learning approach combining spectral visualization and adaptive spectrum selection for signal detection.

## Key findings

- High detection accuracy with limited spectrum observation ranges
- Effective adaptive spectrum selection improves detection efficiency
- Potential for improved cognitive radio spectrum sensing

## Abstract

This paper presents a spectral attention-driven reinforcement learning based intelligent method for effective and efficient detection of important signals in a wideband spectrum. In the work presented in this paper, it is assumed that the modulation technique used is available as a priori knowledge of the targeted important signal. The proposed spectral attention-driven intelligent method is consists of two main components, a spectral correlation function (SCF) based spectral visualization scheme and a spectral attention-driven reinforcement learning mechanism that adaptively selects the spectrum range and implements the intelligent signal detection. Simulations illustrate that the proposed method can achieve high accuracy of signal detection while observation of spectrum is limited to few ranges via effectively selecting the spectrum ranges to be observed. Furthermore, the proposed spectral attention-driven machine learning method can lead to an efficient adaptive intelligent spectrum sensor designs in cognitive radio (CR) receivers.