Novel noise variance and SNR estimation algorithm for wireless MIMO OFDM systems

A new noise variance and SNR estimation algorithm for a 2 /spl times/ 2 MIMO wireless OFDM system as defined in the IST-STINGRAY project is presented. The SNR information is used to adapt parameters or reconfigure parts of the transmitter. The noise variance estimation algorithm uses only 2 OFDM training symbols from each transmitting antenna and the FFT output signals at the receiver. It does not require knowledge of the channel coefficients. Then, using the channel coefficient estimates given by a channel estimator and the estimate of the noise variance, the SNR is computed. The algorithm's performance is measured through Monte-Carlo simulations on a variety of channel models and compared to those of an MMSE algorithm using perfect channel estimates. The normalized MSE of the obtained noise variance estimate shows good results as long as the delay spread of the channel is small enough compared to the OFDM symbol period.

I. Introduction

Diversity systems have been increasingly popular during the last years, with the advent of space-time coding techniques and multiple-input multiple-output (MIMO) systems, due to the diversity gain they can offer. Orthogonal frequency division multiplexing (OFDM), meanwhile, has proven to be useful in frequency-selective channels to achieve high data rates and has been abundantly proposed for high-data rate wireless local area networks (LAN) systems, for example. The combination of OFDM and space-time coding techniques is the current topic of many research projects around the world, for example for fixed wireless access (FWA) applications. This is the focus of the FP6 IST-STINGRAY project, in which the research presented herein takes place. FP6 is the European sixth framework programme, IST the information society technologies and STINGRAY stands for space time coding for reconfigurable wireless access systems. The reconfigurability and adaptivity features of the system will be implemented in both the transmitter and the receiver according to the channel variations and the supported services, through explicit use of feedback channel state information (CSI). The CSI is provided by the receiver to the transmitter. The CSI usually partially or entirely includes or is derived from the signal-to-noise ratio (SNR) information. The estimation of the SNR on each subcarrier as well as the overall SNR is the focus of this paper. The accuracy and reliability of the SNR estimates are important as the performance of the whole system depends on them, through the use of adaptivity and reconfigurability. Through the transmission of training symbols, we must acquire an estimate of the SNR.