Non-data-aided signal-to-noise-ratio estimation

Non-data-aided (NDA) signal-to-noise-ratio (SNR) estimation is considered for binary phase shift keying systems where the data samples are governed by a normal mixture distribution. Inherent estimation accuracy limitations are examined via a simple, closed-form approximation to the associated Cramer-Rao bound which eliminates the need for numerical integration. The expectation-maximization algorithm is proposed to iteratively maximize the NDA likelihood function. Simulation results show that the resulting estimator offers statistical efficiency over a wider range of scenarios than previously published methods.

I. Introduction

Modern wireless communication systems often require knowledge of the receiver signal-to-noise-ratio (SNR). For example, SNR estimates are typically employed in power control, mobile assisted hand-off, adaptive modulation schemes, as well as soft decoding procedures, e.g., [1]–[2]. Many SNR estimators are data-aided (DA) in the sense that known transmit data (such as a training sequence) is used to facilitate the estimation process. However, since the periodic transmission of known data limits system through-put, the problem of non-data-aided (NDA) SNR estimation has also been considered both in terms of performance bounds as well as estimation procedures.