Periodic variation method for blind symbol rate estimation

Abstract

SUMMARY In order to obtain unknown symbol rate of incoming signal at a receiver, in this paper, cyclostationary features of linear digitally modulated signals are exploited by proposed periodic variation method. A low complexity but highly accurate symbol rate estimation technique is obtained. The proposed method is based on a superposed epoch analysis over autocorrelations obtained blindly in different sampling frequencies. The obtained autocorrelations are analyzed in the frequency domain, and it is seen that there are large oscillations when the autocorrelation is obtained around the symbol rate. Then, a superposed epoch analysis is developed in order to estimate symbol rate based of the periodic variations on the frequency responses of autocorrelations. The proposed algorithm is quite accurate in the noisy environment because the noise is having no frequency component after taking Fourier transform of autocorrelations in all sampling rates, and this feature is also valid for the offset frequency that the purposed estimation is not affected by offset frequency. Thus, a successful blind symbol rate estimation algorithm is obtained, and it performs much better error performance than those using the well‐known cyclic correlation based symbol rate estimations, as it is proven by the obtained performances presented in the paper. Copyright © 2013 John Wiley & Sons, Ltd.

Cyclostationarity: New trends and applications

“…Further applications are in the following subjects: capacity evaluation of second-order cyclostationary complex Gaussian noise channels [140], orthogonal overlay channels [371], and power line communication channels [319], timing and other signal parameter estimation [101,134,137,168,224,229,236,246,278,317,334], source separation [16,44,106,169], frequency-domain equalizer (FDE) design [374], blind multiple-input multiple-output (MIMO) system identification [299], signal power (SNR), and signal-to-interference-and-noise ratio (SINR) estimation [8,147,294], Doppler spread estimation [376], microDoppler estimation [214], noise modeling in power lines [130,182,318], and radio frequency interference (RFI) mitigation in radio astronomy [145].…”

Blind Symbol-Rate Estimation and Test Bed Implementation of Linearly Modulated Signals

“…In recent years, cyclostationary-based blind symbol-rate estimation methods have become more popular due to its simplicity in implementation [17]- [21]. The cyclic correlation (CC) method in [22] requires inverse-matrix operation at each discrete Fourier transform (DFT) frequency for low SNR and small excess bandwidth signals, resulting in a very heavy computational burden without providing a proper solution of symbol-rate estimation for OQPSK signals.…”

Accurate low complexity modeling of twisted pairs suitable for G.fast frequencies

“…The G.fast contains several innovative concepts [9], such as a reverse power feeding, a time-division duplex transmission and a vectoring process of transmitted discretemultitone symbols based on Fourier transform [14]. The purpose of all these innovations is to increase the resulting data rate, as concluded by the estimations in [15], through the implementation of modern OFDM techniques [16].…”