Channel-adaptive secure noise modulation for IoT networks

Abstract

This letter introduces channel-adaptive secure noise modulation (CAS-NoiseMod), a novel physical layer security (PLS) scheme for future Internet of Things (IoT) networks. Our approach combines noise modulation with channel-adaptive prescaling, where information bits are encoded through variance power switching of Gaussian waveforms and signals are scaled by the magnitude of the channel between legitimate partners before transmission. This creates an asymmetric advantage where the legitimate receivers benefit from knowledge of pre-scaling and reciprocal channels, while eavesdroppers experience unintelligible noise signals. Theoretical bit error probability (BEP) expressions are derived for the proposed scheme under Rayleigh fading. Our analysis reveals that secure communication is possible with eavesdroppers exhibiting near-random guessing performance, making CAS-NoiseMod suitable for secure IoT networks.