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This article introduces a novel lightweight framework using ambientbackscattering communications to counter eavesdroppers. In particular, ourframework divides an original message into two parts: (i) the active-transmitmessage transmitted by the transmitter using conventional RF signals and (ii)the backscatter message transmitted by an ambient backscatter tag thatbackscatters upon the active signals emitted by the transmitter. Notably, thebackscatter tag does not generate its own signal, making it difficult for aneavesdropper to detect the backscattered signals unless they have priorknowledge of the system. Here, we assume that without decoding/knowing thebackscatter message, the eavesdropper is unable to decode the original message.Even in scenarios where the eavesdropper can capture both messages,reconstructing the original message is a complex task without understanding theintricacies of the message-splitting mechanism. A challenge in our proposedframework is to effectively decode the backscattered signals at the receiver,often accomplished using the maximum likelihood (MLK) approach. However, such amethod may require a complex mathematical model together with perfect channelstate information (CSI). To address this issue, we develop a novel deepmeta-learning-based signal detector that can not only effectively decode theweak backscattered signals without requiring perfect CSI but also quickly adaptto a new wireless environment with very little knowledge. Simulation resultsshow that our proposed learning approach, without requiring perfect CSI andcomplex mathematical model, can achieve a bit error ratio close to that of theMLK-based approach. They also clearly show the efficiency of the proposedapproach in dealing with eavesdropping attacks and the lack of training datafor deep learning models in practical scenarios.

Countering Eavesdroppers with Meta-learning-based Cooperative Ambient Backscatter Communications