Optimal Bit Detection in Thermal Noise Communication Systems Under Rician Fading

TL;DR

This paper develops an exact analytical method for optimal bit detection in thermal noise communication systems affected by Rician fading, improving accuracy over previous Gaussian-based models and aiding energy-efficient IoT receiver design.

Contribution

It introduces a precise detection framework using chi-squared statistics that accounts for fading effects, surpassing prior Gaussian approximations in TNC performance analysis.

Findings

Analytical BEP matches Monte Carlo simulations.

Significant BEP improvements over Gaussian-based detection.

Parameter effects on detection performance quantified.

Abstract

Thermal noise communication (TNC) enables ultra-low-power wireless links for Internet of Things (IoT) devices by modulating the variance of thermal noise, rather than using active carriers. Existing analyses often rely on Gaussian approximations and overlook fading effects, which limits their accuracy. This paper presents an accurate analytical framework for optimal bit detection in TNC systems under Rician fading. Using chi-squared statistics, we derive the optimal maximum-likelihood detection threshold and an expression for the bit error probability (BEP) via Gauss-Laguerre quadrature. The proposed model eliminates approximation errors and accurately characterizes performance for finite sample sizes. Monte Carlo simulations confirm the analytical results and demonstrate significant improvements in BEP compared with suboptimal Gaussian-based detection. Furthermore, the influence of key parameters, sample size, resistance ratio, and Rician K-factor, is quantified. The proposed framework provides a solid foundation for designing energy-efficient TNC receivers in future B5G/6G and large-scale IoT systems.