Non-Data-Aided Parameter Estimation in an Additive White Gaussian Noise Channel

TL;DR

This paper develops new non-data-aided estimators for key parameters in binary-phase-shift-keying over AWGN channels, deriving bounds and demonstrating near-optimal performance with reduced complexity.

Contribution

It introduces a novel low-complexity NDA SNR estimator and provides a comprehensive analysis of parameter bounds and relationships in AWGN channels.

Findings

Proposed estimator performs close to iterative ML estimator

Derived CRLBs for multiple parameters in NDA context

Showed estimator's effectiveness at lower computational cost

Abstract

Non-data-aided (NDA) parameter estimation is considered for binary-phase-shift-keying transmission in an additive white Gaussian noise channel. Cramer-Rao lower bounds (CRLBs) for signal amplitude, noise variance, channel reliability constant and bit-error rate are derived and it is shown how these parameters relate to the signal-to-noise ratio (SNR). An alternative derivation of the iterative maximum likelihood (ML) SNR estimator is presented together with a novel, low complexity NDA SNR estimator. The performance of the proposed estimator is compared to previously suggested estimators and the CRLB. The results show that the proposed estimator performs close to the iterative ML estimator at significantly lower computational complexity.