NN-key: A Neural Network-Based Secret Key for Demapping OFDM Symbols

Generating custom modulation patterns as well as dynamically varying the mapping of the constellation points to their corresponding bit representations are some existing methods for mitigating eavesdropping attacks. In such cases, the custom symbol to bit mapping needs to be conveyed to the receiver through a secure and reliable channel. Instead of sending the representations of the modified symbols in regular information fields, we propose a machine learning-based approach, in which the modified symbols are encoded in the parameters of a light-weight neural network (NN). This NN is trained at the transmitter-side, sent as a secret key to the receiver, where it serves as a demapping block to recover the received symbols correctly. In addition, this paper explores the role of data augmentation during the training stage to increase the robustness of the NN with respect to the noise in the channel, as well as architecture compression to reduce transmission overhead. We validate the robustness of the proposed NN-based custom-modulation demapping approach by comparing it with demapping of a standard scheme (e.g., 16QAM), which reveals no appreciable loss in performance. We further quantitatively analyze the impact of channel and noise impairments on the demapping performance.