Neural Demodulation for Anti-Eavesdropping Embedded Waveforms in IoT Networks

Internet of Things (IoT) devices often deliver sensitive sensing and control data over wireless links that are inherently broadcast, making passive over-the-air eavesdropping a persistent threat even for simple point-to-point transmissions. Many physical-layer security (PLS) techniques, however, rely on accurate channel knowledge, additional spatial degrees of freedom, or strong secrecy assumptions that are often hard to guarantee in practical IoT links. In this paper, we propose an anti-eavesdropping secure transmission scheme by deliberately embedding controllable artificial interference into a conventional BPSK waveform on the same carrier, thereby forming an interference-embedded waveform transmitted over an AWGN channel. To exploit the common architectural asymmetry in IoT systems, the legitimate receiver located at an IoT gateway/edge node employs an Anti-Eavesdropping Waveform Demodulator (AEWD), a ResNet–self-attention–BiLSTM neural demodulator trained end-to-end to recover bits directly from raw I/Q samples without explicit interference parameter estimation. Under identical channel conditions, conventional model-based receivers that perform deterministic interference suppression followed by demodulation incur substantial BER degradation and frequently exhibit interference-limited error floors. Extensive simulations across a wide range of SNR and SIR demonstrate that AEWD consistently outperforms classical baselines under both single-tone and multi-tone nonstationary interference. The results suggest that waveform-level interference embedding combined with a dedicated neural demodulator can create a practical receiver performance gap to strengthen confidentiality for IoT communications.