Symbol Rate Estimation with Inverse Fourier Transforms

Flohberger, Markus; Kogler, Wolfgang; Gappmair, Wilfried; Koudelka, Otto

doi:10.1109/iwssc.2006.256003

Cited by 22 publications

(12 citation statements)

References 11 publications

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“…It is noted that the performance of the proposed estimator improves with an increase in the value of the oversampling factor for both OQPSK and QPSK signals. It is because the frequency resolution is directly proportional to the oversampling factor [15]. It is worthwhile to mention that the resolution cannot be reduced completely just by increasing the value of the oversampling factor due to the memory constraint limitation.…”

Section: Simulation and Measurement Resultsmentioning

confidence: 99%

“…For the same reason, the filter bank method [13] does not apply for OQPSK. The IFT of the PSD of OQPSK signals provides |g(t) + g(t − T /2)| 2 instead of |g(t)| 2 for QPSK signals given in [15]. The pulse shape |g(t) + g(t − T /2)| 2 does not provide first minimum at t = T , which is the usual case for QPSK signals where first minimum is used to estimate the symbol rate.…”

Section: B Existing Methods and Their Limitationsmentioning

confidence: 99%

“…If F s is not an integer multiple of f s , the estimation performance degrades due to the low frequency resolution, and it commonly occurs for a high sampling rate and a short-burst signal. Several methods, such as zero padding and interpolation, have been used to increase the frequency resolution and to improve the estimation performance [13], [15].…”

Section: System Modelmentioning

confidence: 99%

“…An algorithm based on wavelet transform is proposed in [12], which requires prior knowledge of the modulation scheme. Another method based on inverse Fourier transform (IFT) consists of a two-stage process: coarse and fine estimations [15]. Coarse estimation gives rough knowledge of symbol rate, and fine estimation improves its performance through a closed-loop timing recovery process.…”

Section: Introductionmentioning

confidence: 99%

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

Majhi

2015

IEEE Trans. Veh. Technol.

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Although several blind symbol-rate estimation methods have been proposed for linearly modulated signals, none of these methods are applicable to offset quadrature phase-shift keying (OQPSK)-modulated signals. The frequently used cyclic correlation (CC) methods are unable to estimate the symbol rate for OQPSK signals as the cyclic peak at the symbol-rate frequency disappears due to the presence of an offset between the in-phase (I) and quadrature (Q) components of OQPSK signals. In this paper, a blind symbol-rate estimation for linearly modulated signals has been proposed. The symbol rate of OQPSK signals has also been estimated with no knowledge of modulation schemes. The offset between I and Q components of the received signals is compensated for by using a set of approximate offsets in obtaining the cyclic peak at the symbol-rate frequency. The proposed estimation has a robust performance in the presence of unknown frequency offset, timing offset, and carrier phase. The algorithm is implemented on a test bed to provide experimental results.Index Terms-Baud rate estimation, blind symbol-rate estimation, cyclic correlation (CC), measurement, offset quadrature phase-shift keying (OQPSK), test statistics, test-bed implementation.

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Section: Simulation and Measurement Resultsmentioning

confidence: 99%

Section: B Existing Methods and Their Limitationsmentioning

confidence: 99%

Section: System Modelmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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

Majhi

2015

IEEE Trans. Veh. Technol.

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“…The receiver must estimate this parameter from the observed data to demodulate the input signals correctly. At present, several symbol rate estimation methods have been widely used, such as the wavelet transform [1], the cyclic correlation [2], and inverse fast Fourier transform (IFFT) [3]. However, all these methods can bring estimation error more or less.…”

Section: Introductionmentioning

confidence: 99%

The effects of symbol rate offset on the BER performance of 16QAM signals

Yang

Zhu

2009

2009 International Conference on Communications, Circuits and Systems

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In non-cooperative communication we need to estimate of symbol rate blindly. Hence, it is necessary to correct symbol rate offset properly and estimate its effects on the receiving performance. In the paper, the symbol timing recovery loop using an acquisition-aiding technique (that is, adaptively adjusting loop bandwidth) can capture symbol rate offset of 1% with the help of the lock detector for 16QAM signals. We analyze and simulate the effects for symbol rate offset of ± 0.5% and ± 1% on bit error rate (BER) performance over an additive white Gaussian noise (AWGN). The results show that under the same BER the real-time demodulation loss is less than 0.5dB compared to the theoretical value. In addition, an out-of-Iock detector is proposed. Together with the lock detector, the out-of-Iock detector can ensure the symbol timing recovery loop provide good steady performance and relatively fast convergence rate by adaptively adjusting loop bandwidth.

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Periodic variation method for blind symbol rate estimation

Güner

Kaya

2013

Int J Communication

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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.

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Symbol Rate Estimation with Inverse Fourier Transforms

Cited by 22 publications

References 11 publications

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

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

The effects of symbol rate offset on the BER performance of 16QAM signals

Periodic variation method for blind symbol rate estimation

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