Low-Complexity Pearson-Based Detection for AWGN Channels with Offset
Antonino Favano, Luca Barletta, Marco Sforzin, Paolo Amato, Marco Ferrari · 2024
This work investigates the error performance of detection schemes based on the minimum Pearson distance in the context of additive white Gaussian noise channels with unknown and unbounded offset, constant throughout each channel use. We derive a lower bound on the word error rate under modified Pearson (MP) detection. Additionally, we introduce a new and low-complexity detection strategy, namely the Simplified Pearson (SP) detector. We analyze and compare the error performance of the SP detector with that of the MP detector.