Adaptive Expectation-Maximization Detection of Gaussian Signals in Quantized Systems with Jointly Unknown Statistical Parameters
DOI:
https://doi.org/10.65405/k9dafk60Keywords:
Gaussian signals, EM algorithm, signal detec- tion, unknown parameters, quantization, low-resolution ADC, GLRT, 6G.Abstract
This paper addresses the challenging problem of detecting Gaussian signals with jointly unknown statistical pa- rameters (mean, variance) in the presence of additive white Gaussian noise (AWGN) and low-resolution quantization. We propose a novel detection scheme based on the Expectation- Maximization (EM) algorithm, which iteratively estimates the unknown parameters from the quantized observations. The un- quantized received signal is treated as a latent variable, allowing for a tractable derivation of the E-step and M-step. Subsequently, an EM-based Generalized Likelihood Ratio Test (GLRT) detector is formulated. The performance of the proposed detector is analyzed through simulations, demonstrating its effectiveness in various quantization scenarios and comparing it against theoretical benchmarks. This work provides a robust solution for signal detection in power-constrained and high-frequency communication systems, such as those envisioned for 6G, where low-resolution analog-to-digital converters (ADCs) are prevalent
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