In this study, we present the implementation of a neural network model capable of classifying radio frequency identification (RFID) tags based on their electromagnetic (EM) signature for authentication applications. One important application of the chipless RFID addresses the counterfeiting threat for manufacturers. The goal is to design and implement chipless RFID tags that possess a unique and unclonable fingerprint to authenticate objects. As EM characteristics are employed, these fingerprints cannot be easily spoofed. A set of 18 tags operating in V band (65–72 GHz) was designed and measured. V band is more sensitive to dimensional variations compared to other applications at lower frequencies, thus it is suitable to highlight the differences between the EM signatures. Machine learning (ML) approaches are used to characterize and classify the 18 EM responses in order to validate the authentication method. The proposed supervised method reached a maximum recognition rate of 100%, surpassing in terms of accuracy most of RFID fingerprinting related work. To determine the best network configuration, we used a random search algorithm. Further tuning was conducted by comparing the results of different learning algorithms in terms of accuracy and loss.
Classical, i.e. non-quantum, communications include configurations with multiple-input multiple-output (MIMO) channels. Some associated signal processing tasks consider these channels in a symmetric way, i.e. by assigning the same role to all channel inputs, and similarly to all channel outputs. These tasks especially include channel identification/estimation and channel equalization, tightly connected with source separation. Their most challenging version is the blind one, i.e. when the receivers have (almost) no prior knowledge about the emitted signals. Other signal processing tasks consider classical communication channels in an asymmetric way. This especially includes the situation when data are sent by Emitter 1 to Receiver 1 through a main channel, and an "intruder" (including Receiver 2) interferes with that channel so as to extract information, thus performing so-called eavesdropping, while Receiver 1 may aim at detecting that intrusion. Part of the above processing
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