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IPFSCNN: A Time-Frequency Fusion CNN for Wideband Spectrum Sensing.

Soon-Young Kwon¹, Do-Hyun Park¹, Hyoung-Nam Kim¹

¹School of Electrical and Electronics Engineering, Pusan National University, Busan 46241, Republic of Korea.

Sensors (Basel, Switzerland)

|December 11, 2025

Summary

This study introduces a novel deep learning model, IQ-Parallel FFT-Serial CNN (IPFSCNN), for cognitive radio spectrum sensing. IPFSCNN enhances detection performance, especially in low signal conditions, by fusing time-domain and frequency-domain data.

Keywords:

IQ/FFT fusion cognitive radio wideband spectrum sensing

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Area of Science:

Electrical Engineering
Computer Science
Signal Processing

Background:

Cognitive radio requires efficient spectrum sensing for optimal frequency resource utilization.
Existing deep learning models often use only time-domain (I/Q) or frequency-domain (FFT) data, limiting performance.
A hybrid approach fusing both data types is needed to improve wideband spectrum sensing.

Purpose of the Study:

To propose a novel asymmetric hybrid deep learning architecture, IQ-Parallel FFT-Serial CNN (IPFSCNN), for wideband spectrum sensing.
To synergistically fuse I/Q and FFT data representations for enhanced multi-label classification.
To evaluate IPFSCNN's performance against state-of-the-art models, focusing on accuracy and computational efficiency.

Main Methods:

Developed an asymmetric CNN architecture (IPFSCNN) with parallelized I/Q data stream and serial FFT data stream.
Fused temporal features from I/Q data and spectral patterns from FFT data.
Conducted experiments using an LTE-M dataset for performance evaluation and comparison.

Main Results:

IPFSCNN demonstrated superior detection performance compared to DeepSense and ParallelCNN, particularly in low signal-to-noise ratio (SNR) environments.
The proposed model achieved higher accuracy with reduced computational complexity, using 15% fewer parameters and one-third of the MAC operations versus DeepSense.
An ablation study confirmed the superiority of the 'IQ-Parallel FFT-Serial' configuration over other hybrid approaches.

Conclusions:

The asymmetric hybrid architecture (IPFSCNN) effectively fuses I/Q and FFT data for improved wideband spectrum sensing in cognitive radio.
IPFSCNN offers a computationally efficient and high-performance solution for spectrum sensing, outperforming existing methods.
The findings highlight the advantage of combining parallel and serial processing streams for different data modalities in deep learning for signal processing.