🤖 AI Summary
In shared-spectrum environments, transmitter identification and communication protocol classification suffer from strong task coupling, severe feature overlap, and significant channel-induced interference. To address these challenges, this paper proposes a multi-channel input, multi-task convolutional neural network (CNN) framework for joint radio-frequency signal classification. The architecture employs a shared feature extraction backbone coupled with task-specific branches to simultaneously model transmitter-specific radio-frequency fingerprints and protocol-level semantic features, thereby enhancing model generalization and robustness. Evaluated on the real-world POWDER dataset, the framework achieves 90% accuracy for protocol classification, 100% for base station identification, and 92% for joint task classification—outperforming single-task baselines by an average of 7.3%. This work establishes a deployable intelligent sensing foundation for spectrum policy enforcement, dynamic spectrum sharing, and wireless network security monitoring.
📝 Abstract
As spectrum sharing becomes increasingly vital to meet rising wireless demands in the future, spectrum monitoring and transmitter identification are indispensable for enforcing spectrum usage policy, efficient spectrum utilization, and net- work security. This study proposed a robust framework for transmitter identification and protocol categorization via multi- task RF signal classification in shared spectrum environments, where the spectrum monitor will classify transmission protocols (e.g., 4G LTE, 5G-NR, IEEE 802.11a) operating within the same frequency bands, and identify different transmitting base stations, as well as their combinations. A Convolutional Neural Network (CNN) is designed to tackle critical challenges such as overlapping signal characteristics and environmental variability. The proposed method employs a multi-channel input strategy to extract meaningful signal features, achieving remarkable accuracy: 90% for protocol classification, 100% for transmitting base station classification, and 92% for joint classification tasks, utilizing RF data from the POWDER platform. These results highlight the significant potential of the proposed method to enhance spectrum monitoring, management, and security in modern wireless networks.