Transmitter Identification and Protocol Categorization in Shared Spectrum via Multi-Task RF Classification at the Network Edge
This addresses spectrum monitoring and security challenges for wireless network operators and regulators, though it appears incremental as it applies existing CNN methods to a specific domain problem.
The study tackled the problem of identifying transmitters and categorizing protocols in shared spectrum environments using a multi-task RF classification framework, achieving 90% accuracy for protocol classification, 100% for base station classification, and 92% for joint classification tasks.
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 network 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.