Learning to Identify RF Devices from Few Pilots via Reptile

Le résumé fourni par la source

We examine a neural network-based system designed to identify$K$devices, each with device-specific I/Q imbalances in the RF modulator, as they transmit signals to an access point using time-division multiple access. Traditional methods begin training with randomly chosen initial parameters and need numerous pilot symbols, which can be impractical in scenarios such as when a unmanned aerial vehicle (UAV) serves as the access point, collecting data from massive device sensor networks. In this work, we focus on adaptive learning with minimal pilot symbols to train the neural network to classify devices by their I/Q imbalances. Reptile is an efficient meta-learning algorithm designed to help models rapidly adjust to new tasks with minimal training data. By employing Reptile, we pre-train neural network model parameters offline, facilitating rapid online adaptation and fine-tuning for effective identification of new devices. Simulations indicate that Reptile surpasses conventional methods in device identification with fewer pilot symbols.

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