Low complexity joint estimation of synchronization impairments in sparse channel for MIMO–OFDM system

Jose, Renu; Ambat, Sooraj K.; Hari, K.V.S.

doi:10.1016/j.aeue.2013.07.012

Cited by 17 publications

(18 citation statements)

References 20 publications

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“…Recall that the NR method is applied in our algorithm for N 0 different initial conditions. Finally, the desired root is selected by evaluating (14), which requires N 0 L complex exponents, N 0 L complex multiplications, N 0 .L 1/ real additions, and N 0 real comparisons.…”

Section: D2 Computational Complexity Analysis For the Aopse Of Subsmentioning

confidence: 99%

Low complexity estimation of carrier and sampling frequency offsets in burst‐mode OFDM systems

Murin

Dabora

2015

Wireless Communications

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We study communications under slowly varying channels, and consider three cases of knowledge of the channel impulse response (CIR): full knowledge, no knowledge, and partial knowledge of the CIR. By partial knowledge, we refer to knowing only either the CIR magnitudes or the CIR phases. It is known that obtaining the exact joint maximum‐likelihood estimate (MLE) of the CFO and the SFO requires a two‐dimensional search. Here, we present a new estimation method which uses the Taylor expansion of the MLE cost function, combined with the best linear unbiased estimator, to obtain a method which does not require such a search. The computational complexity of the new method is evaluated. Numerical simulations demonstrate that the new method approaches the corresponding Cramér‐Rao bound for a wide range of signal‐to‐noise ratios, and has superior performance compared to all other existing methods for approximating the solution for the joint MLE, while maintaining a low computational complexity. Copyright © 2015 John Wiley & Sons, Ltd.

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Section: D2 Computational Complexity Analysis For the Aopse Of Subsmentioning

confidence: 99%

Low complexity estimation of carrier and sampling frequency offsets in burst‐mode OFDM systems

Murin

Dabora

2015

Wireless Communications

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“…It has been one of the fastest growing research areas in signal processing, and has been studied in many application fields, e.g., imaging, channel estimation, radar, face recognition [2][3][4][5][6].…”

Section: Introductionmentioning

confidence: 99%

Information-theoretic analysis of support recovery from sparsely corrupted measurements

Tian

et al. 2015

AEU - International Journal of Electronics and Communications

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“…To maximize the advantages of MIMO-OFDM, it is therefore of crucial importance to accurately estimate the CFO and SFO [22,23]. In the literature, a number of pilot-assisted approaches, from maximum likelihood estimation (MLE) to linear least-squares estimation (LSE) strategies [22][23][24][25][26][27][28][29][30][31][32][33][34][35], have been presented to obtain accurate CFO and SFO estimates. The MLE is well known to be capable of achieving high accuracy in joint estimation of CFO and SFO, but its high complexity limits its practical use [22][23][24][25].…”

mentioning

confidence: 99%

“…The MLE is well known to be capable of achieving high accuracy in joint estimation of CFO and SFO, but its high complexity limits its practical use [22][23][24][25]. To solve the complexity issue, a considerable amount of research has been devoted to the low complexity variants of joint MLE [26][27][28][29][30][31][32][33][34][35]. In [26][27][28][29][30][31][32], the ad-hoc-based CFO and SFO estimation strategy was developed calculating the phase difference from two consecutive pilot symbols with symmetric and uniform distribution across DC.…”

mentioning

confidence: 99%

“…In [26][27][28][29][30][31][32], the ad-hoc-based CFO and SFO estimation strategy was developed calculating the phase difference from two consecutive pilot symbols with symmetric and uniform distribution across DC. In [33][34][35], a decoupled estimation of CFO and SFO was proposed, which is still computation-intensive for real-time processing. Hence, more efficient frequency estimation and its complete performance analysis in a multipath fading channel is a key challenge in realizing high-performance MIMO-OFDM receivers.In this paper, the performance analysis of joint CFO and SFO estimator for the MIMO-OFDM ATSC broadcast system is presented in a the multipath fading channel.…”

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confidence: 99%

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Efficient Joint Estimation of Carrier Frequency and Sampling Frequency Offsets for MIMO-OFDM ATSC Systems

Jung

You

2018

Symmetry

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Multiple-input multiple-output orthogonal frequency division multiplexing (MIMO-OFDM) is appealing for the provision of high spectral efficiency in digital terrestrial broadcast systems. To fully obtain its advantageous features, it is very important to remove the frequency mismatch between the transmitter and the receiver. In this paper, we present the performance analysis of joint estimation of carrier and sampling frequency offsets in the MIMO-OFDM-based advanced television systems committee (ATSC) 3.0 system. In the MIMO-OFDM ATSC system, the continual pilot (CP) is primarily utilized to perform frequency synchronization. To efficiently suppress an unwanted bias introduced by the presence of random-likely located CPs, an optimal pilot subset is selected to form the basis of least squares frequency-offset estimation. A closed-form mean squared error is derived in the context of MIMO-OFDM, considering the multipath fading channel. We show via computer simulations and numerical analysis that the proposed estimation method achieves higher estimation accuracy than the existing estimation method.Despite many advantageous features, the OFDM-based ATSC 3.0 system has a few key issues that have to be addressed. One of the biggest weaknesses of OFDM is its vulnerability to synchronization mismatches, including symbol timing offset (STO), carrier frequency offset (CFO), and sampling frequency offset (SFO) in a frequency-selective fading environment [17]. If these effects are not properly compensated, OFDM systems can suffer from severe inter-symbol interference (ISI) or inter-channel interference (ICI), which downgrades the performance of OFDM and MIMO-OFDM systems. To eliminate ICI and ISI effects, the receiver must first estimate the correct STO [18,19]. Second, it is essential that the frequency offset on the OFDM block is estimated and corrected to remove ICI. In particular, the performance of OFDM receivers is greatly affected by SFO as the fast Fourier transform (FFT) size becomes larger [20], which usually occurs in a typical broadcast system. Therefore, the challenges in the MIMO-OFDM-based ATSC 3.0 system are its susceptibility to the frequency synchronization between the transmitter and the receiver. In order to help acquire synchronization, the ATSC 3.0 system provides several types of pilot symbols such as scattered pilots (SPs), edge pilots (EPs), continual pilots (CPs), and subframe boundary pilots (SBPs) [10]. The SPs, EPs, and SBPs are mainly dedicated to estimate the channel, whereas CPs are present to assist time and frequency estimation [21]. To maximize the advantages of MIMO-OFDM, it is therefore of crucial importance to accurately estimate the CFO and SFO [22,23]. In the literature, a number of pilot-assisted approaches, from maximum likelihood estimation (MLE) to linear least-squares estimation (LSE) strategies [22][23][24][25][26][27][28][29][30][31][32][33][34][35], have been presented to obtain accurate CFO and SFO estimates. The MLE is well known to be capable of achieving high accuracy in joi...

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Low complexity joint estimation of synchronization impairments in sparse channel for MIMO–OFDM system

Cited by 17 publications

References 20 publications

Low complexity estimation of carrier and sampling frequency offsets in burst‐mode OFDM systems

Low complexity estimation of carrier and sampling frequency offsets in burst‐mode OFDM systems

Information-theoretic analysis of support recovery from sparsely corrupted measurements

Efficient Joint Estimation of Carrier Frequency and Sampling Frequency Offsets for MIMO-OFDM ATSC Systems

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