Radio frequency fingerprinting method and system based on convolution-attention mechanism and multi-packet inference

A radio frequency fingerprinting system based on a convolution-attention mechanism and multi-packet inference, comprising:

• a preprocessing module, configured to capture a device transmission signal, and preprocess the transmission signal to obtain a spectrogram;
• a model training module, configured to construct a radio frequency fingerprinting model, and input the obtained spectrogram into the radio frequency fingerprinting model for training, to obtain a trained radio frequency fingerprinting model, wherein
• the radio frequency fingerprinting model comprises a convolutional layer, a positional encoding layer, and a Transformer encoder;
• the convolutional layer transforms an inputted feature map into a high-order feature representation;
• the positional encoding layer is configured to add position information;
• the Transformer encoder comprises a multi-head self-attention mechanism and a feed-forward neural network layer, the multi-head self-attention mechanism is used for capturing global dependencies that span an entire sequence, and the feed-forward neural network layer further enhances a feature representation capability;
• the convolutional layer comprises a standardization layer, a point convolutional layer, a GLU activation function layer, a depthwise separable convolutional layer, a batch normalization layer, a Swish activation function layer, a concatenated convolutional layer, and a Dropout layer;
• firstly, the obtained spectrogram is inputted into the convolutional layer, to standardize the inputted spectrogram;
• a channel dimension is adjusted by employing 1×1 convolutions through the point convolutional layer, to obtain a feature map with an adjusted number of channels;
• key information of the feature map is enhanced through the GLU activation function layer while redundant information of the feature map is suppressed;
• parameters of the feature map are reduced through the depthwise separable convolutional layer while a spatial feature is enhanced;
• a feature map with a stable distribution is obtained through the batch normalization layer;
• an activated feature map is obtained through the Swish activation function layer while subtle gradient information of a negative value region is preserved;
• a final feature representation with an optimized number of channels is obtained through the concatenated convolutional layer;
• and finally, a regularized feature map X is obtained through the Dropout layer;
• then, the feature map is introduced into the positional encoding layer after being subjected to operations at the convolutional layer, to obtain a feature map subjected to positional encoding, and a time relationship present in the data is captured;
• finally, the feature map subjected to positional encoding is inputted into the Transformer encoder, and the global dependencies that span the entire sequence are captured through the Transformer encoder by employing the multi-head self-attention mechanism;
• and
• after the processing by the Transformer encoder, a time dimension of the feature map is compressed into a feature vector with a fixed length through a one-dimensional global average pooling layer;
• and finally the feature vector is fed into a fully connected layer and a Softmax layer for classification, and the trained RFF-CAT model, i.e., the radio frequency fingerprinting model, is obtained after training;
• and
• a multi-packet inference module, configured to perform prediction by employing the trained radio frequency fingerprinting model, to obtain a final prediction result by incorporating a multi-packet inference method, wherein
• the multi-packet inference method comprises:
• dividing inputted radio frequency signal data into a plurality of data packets, calculating an initial prediction value of each data packet, i.e., obtaining an initial prediction value through the RFF-CAT model, defining an initial weight set, and then performing weighted summation, to obtain a fused prediction probability;
• calculating an error between the initial prediction value and the fused prediction probability, and updating a weight to adapt to the prediction value of each data packet;
• and
• performing weighted summation again on the prediction value of each data packet by employing the updated weight, to obtain a new fused prediction probability, i.e., identifying a radio frequency fingerprint of a corresponding device, to achieve device authentication and identification.