Non-data-aided SNR Estimation for QPSK Modulation in AWGN Channel

TL;DR

This paper introduces a modified non-data-aided SNR estimation algorithm for QPSK signals that improves accuracy and reduces bias at low sample sizes and negative SNR values, outperforming existing methods.

Contribution

The paper proposes a novel modification to the SVR algorithm for NDA SNR estimation that enhances performance in challenging conditions.

Findings

The new algorithm outperforms M2M4 and original SVR in NMSE and bias.

It maintains similar complexity to existing algorithms.

Effective at low SNR and small sample sizes.

Abstract

Signal-to-noise ratio (SNR) estimation is an important parameter that is required in any receiver or communication systems. It can be computed either by a pilot signal data-aided approach in which the transmitted signal would be known to the receiver, or without any knowledge of the transmitted signal, which is a non-data-aided (NDA) estimation approach. In this paper, a NDA SNR estimation algorithm for QPSK signal is proposed. The proposed algorithm modifies the existing Signal- to-Variation Ratio (SVR) SNR estimation algorithm in the aim to reduce its bias and mean square error in case of negative SNR values at low number of samples of it. We first present the existing SVR algorithm and then show the mathematical derivation of the new NDA algorithm. In addition, we compare our algorithm to two baselines estimation methods, namely the M2M4 and SVR algorithms, using different test cases. Those test cases include low SNR values, extremely high SNR values and low number of samples. Results showed that our algorithm had a better performance compared to second and fourth moment estimation (M2M4) and original SVR algorithms in terms of normalized mean square error (NMSE) and bias estimation while keeping almost the same complexity as the original algorithms