CiteULike: dcastro's snr

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

S Boumard — 2008-05-19T19:45:22-00:00

Global Telecommunications Conference, 2003. GLOBECOM '03. IEEE, Vol. 3 (2003), pp. 1330-1334 vol.3.

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.

SNR estimation for non-constant modulus constellations

Ping Gao — 2008-05-09T23:49:08-00:00

Wireless Communications and Networking Conference, 2004. WCNC. 2004 IEEE, Vol. 1 (2004), pp. 24-29 Vol.1.

We propose a new technique to estimate the signal-to-noise ratio (SNR) over the flat-fading channel. This is a non-data-aided, envelope-based estimator that can be applied to non-constant modulus constellations which is a feature not found in the existing approaches. We also analyze the performance of our estimators for both PSK and non-PSK constellations by deriving their asymptotic variances and comparing it with the Cramer-Rao bounds (CRBs). Moreover, we discuss how the SNR estimates can be used to approximate the bit error rate (BER) and how the accuracy of the SNR estimate is related to that of the BER estimate, which justifies the necessity for the accurate estimation of SNR. The analytical performance is shown for both PSK and non-PSK constellations, such as 8 QAM. and 16 QAM. Monte Carlo simulation results corroborate our analysis.

Simple SNR estimation methods for QPSK modulated short bursts

Dong-Joon Shin — 2008-05-09T23:47:10-00:00

Global Telecommunications Conference, 2001. GLOBECOM '01. IEEE, Vol. 6 (2001), pp. 3644-3647 vol.6.

An accurate signal-to-noise ratio (SNR) measurement is essential for cellular and satellite communication receivers for various purposes, including the demodulation algorithm adaptation to the time-varying channel conditions. In this paper, computationally simple SNR estimation methods, called the error vector magnitude (EVM) method and the minimum mean-square error estimation (MMSE) method are proposed. Analytic derivations as well as simulation results are presented to demonstrate the performance of the proposed schemes

A new SNR's estimator for QPSK Modulations in an AWGN channel

Guangliang Ren — 2008-05-09T23:47:09-00:00

Circuits and Systems II: Express Briefs, IEEE Transactions on [see also Circuits and Systems II: Analog and Digital Signal Processing, IEEE Transactions on], Vol. 52, No. 6. (2005), pp. 336-338.

Signal-to-noise ratio (SNR) measurement is required in many receivers and systems of communications. In this brief, we derive a new SNR's estimator based on the second and fourth-order moments of the complex signals of quadrature phase-shift keying modulations. The mean square error of the estimated SNR by the proposed algorithm is smaller than that by other methods for a low SNR with limited samples.

SNR estimation in frequency domain using circular correlation

Dae-Ki Hong — 2008-05-09T23:47:08-00:00

Electronics Letters, Vol. 38, No. 25. (2002), pp. 1693-1694.

Beaulieu et al. (2000) proposed four signal-to-noise ratio (SNR) estimators in the time domain. The authors propose SNR estimators for an M-ary phase shift keying (MPSK) modulation in the frequency domain. A discrete Fourier transform (DFT) algorithm is used for the frequency domain analysis of the received signal. The frequency spectra enable biased SNR estimation. Circular correlation is used for robust and fast SNR estimation when the frequency offset occurs at the received signal. Simulation results show that the new estimators present good performance even when a large frequency offset occurs.

Comparison of four SNR estimators for QPSK modulations

NC Beaulieu — 2007-10-14T18:27:39-00:00

Communications Letters, IEEE, Vol. 4, No. 2. (2000), pp. 43-45.

Four signal-to-noise ratio (SNR) estimators for quaternary phase-shift keying (QPSK)-like signaling are proposed and examined. Two are based on receiver statistics directly related to the SNR while two others are based on receiver statistics inversely related to the SNR. The results show that the estimators based on the inverse of the SNR perform better than the estimators based on the SNR

A comparison of SNR estimation techniques for the AWGN channel

DR Pauluzzi — 2006-08-30T15:24:36-00:00

Communications, IEEE Transactions on, Vol. 48, No. 10. (2000), pp. 1681-1691.

The performances of several signal-to noise ratio (SNR) estimation techniques reported in the literature are compared to identify the "best" estimator. The SNR estimators are investigated by the computer simulation of baseband binary phase-shift keying (PSK) signals in real additive white Gaussian noise (AWGN) and baseband 8-PSK signals in complex AWGN. The mean square error is used as a measure of performance. In addition to comparing the relative performances, the absolute levels of performance are also established; the simulated performances are compared to a published Cramer-Rao bound (CRB) for real AWGN and a CRB for complex AWGN that is derived here. Some known estimator structures are modified to perform better on the channel of interest. Estimator structures for both real and complex channels are examined.