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Open Access

ARTICLE

BCAM-Net: A Bidirectional Cross-Attention Multimodal Network for IoT Spectrum Sensing under Generalized Gaussian Noise

Yuzhou Han1,*, Zhuoran Li1, Ahmad Gendia2,3, Teruji Ide4, Osamu Muta2,*
1 Graduate School of Information Science and Electrical Engineering, Kyushu University, Fukuoka, Japan
2 Faculty of Information Science and Electrical Engineering, Kyushu University, Fukuoka, Japan
3 Electrical Engineering Department, Faculty of Engineering, Al-Azhar University, Cairo, Egypt
4 National Institute of Technology, Kagoshima College, Kagoshima, Japan
* Corresponding Author: Yuzhou Han. Email: email-u; Osamu Muta. Email: email
(This article belongs to the Special Issue: Advancements in Mobile Computing for the Internet of Things: Architectures, Applications, and Challenges)

Computers, Materials & Continua https://doi.org/10.32604/cmc.2026.076555

Received 22 November 2025; Accepted 20 January 2026; Published online 18 February 2026

Abstract

Spectrum sensing is an indispensable core part of cognitive radio dynamic spectrum access (DSA) and a key approach to alleviating spectrum scarcity in the Internet of Things (IoT). The key issue in practical IoT networks is robust sensing under the coexistence of low signal-to-noise ratios (SNRs) and non-Gaussian impulsive noise, where observations may be distorted differently across feature modalities, making conventional fusion unstable and degrading detection reliability. To address this challenge, the generalized Gaussian distribution (GGD) is adopted as the noise model, and a multimodal fusion framework termed BCAM-Net (bidirectional cross-attention multimodal network) is proposed. BCAM-Net adopts a parallel dual-branch architecture: a time-frequency branch that leverages the continuous wavelet transform (CWT) to extract time-frequency representations, and a temporal branch that learns long-range dependencies from raw signals. BCAM-Net utilizes a bidirectional cross-attention mechanism to achieve deep alignment and mutual calibration of temporal and time-frequency features, generating a fused representation that is highly robust to complex noise. Simulation results show that, under GGD noise with shape parameter β=0.5, BCAM-Net achieves high detection probabilities in the low-SNR regime and outperforms representative baselines. At a false alarm probability Pf=0.1 and SNR of −14 dB, it attains a detection probability of 0.9020, exceeding the CNN-Transformer, WT-ResNet, TFCFN, and conventional CNN benchmarks by 5.75%, 6.98%, 33.3%, and 21.1%, respectively. These results indicate that BCAM-Net can effectively improve spectrum sensing performance in low-SNR impulsive-noise scenarios, and provides a lightweight, high-performance solution for practical cognitive radio spectrum sensing.

Keywords

Cognitive radio; spectrum sensing; IoT; deep learning; bidirectional cross-attention; multimodal fusion

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