Carrier Frequency Offset Estimation using Data-Dependent Superimposed Training

Moosvi, S. M. A.; McLernon, Des; Orozco-Lugo, A.G.; Lara, M.; Ghogho, Mounir

doi:10.1109/icdsp.2007.4288594

Cited by 5 publications

(6 citation statements)

References 9 publications

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“…From (20) and (22), however, we notice that the interference vector due to information sequence can hardly be neglected since the power of data symbol is much larger than the pilot power.…”

Section: St-based Channel Estimation Over One Ofdm Symbolmentioning

confidence: 98%

“…In ST-based approaches [8,[13][14][15][16][17][18][19][20][21][22][23], the pilot symbols are superimposed (arithmetically added) to the information sequences as…”

Section: St-based Channel Estimationmentioning

confidence: 99%

“…Consequently, as the frame length Ω increases, the LTV channel estimation variance (39) approaches to a fixed lower-bound associated with the system Doppler frequency as well as the information to pilot power ratio. This is quite different from the existing ST-based channel estimation approaches [8,[13][14][15][16][17][18][19][20][21][22][23].…”

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confidence: 98%

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Linearly time-varying channel estimation and training power allocation for OFDM/MIMO systems using superimposed training

Zhang

Dai²,

Pan

2011

Sci. China Inf. Sci.

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We address the problem of estimating the linearly time-varying (LTV) channel of orthogonal frequency division multiplexing (OFDM)/multiple-input multiple-output (MIMO) systems using superimposed training (ST). The LTV channel is modeled by truncated discrete Fourier bases. Based on this model, a two-step approach is adopted to estimate the LTV channel over multiple OFDM symbols. We also present performance analysis of the channel estimation and derive a closed-form expression for the channel estimation variances. It is shown that the estimation variances, unlike that of the conventional ST-based schemes, approach to a fixed lower-bound as the training length increases, which is directly proportional to information-pilot power ratios. For wireless communication systems with a limited transmission power, we optimize the ST power allocation by maximizing the lower bound of the average channel capacity. Simulation results show that the proposed approach outperforms the frequency-division multiplexed training schemes. CitationZhang H, Dai X H, Pan D R. Linearly time-varying channel estimation and training power allocation for OFDM/MIMO systems using superimposed training.

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“…From (20) and (22), however, we notice that the interference vector due to information sequence can hardly be neglected since the power of data symbol is much larger than the pilot power.…”

Section: St-based Channel Estimation Over One Ofdm Symbolmentioning

confidence: 98%

“…In ST-based approaches [8,[13][14][15][16][17][18][19][20][21][22][23], the pilot symbols are superimposed (arithmetically added) to the information sequences as…”

Section: St-based Channel Estimationmentioning

confidence: 99%

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Linearly time-varying channel estimation and training power allocation for OFDM/MIMO systems using superimposed training

Zhang

Dai²,

Pan

2011

Sci. China Inf. Sci.

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“…So, we consider proposing a CFO estimation algorithm based on DDST scheme for OFDM systems. Reference [4] has proposed a DDST based CFO estimation algorithm but it cannot be directly used to OFDM system for the mulling of input symbols at the pilot subcarriers cannot be recovered at the receiver, which have been discussed in [3]. Inspired by [3], a DDST scheme based fractional CFO estimator is proposed in this paper.…”

Section: Introductionmentioning

confidence: 97%

Carrier frequency offset estimation based on data-dependent superimposed training for OFDM systems

Zhang

Muqing

Guo

et al. 2012

2012 19th International Conference on Telecommunications (ICT)

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In this paper, we propose a solution to the carrier frequency offset (CFO) estimation based on data dependent superimposed training scheme for Orthogonal Frequency Division Multiplexing (OFDM) systems. The proposed CFO estimation can improve the bandwidth efficiency compared with traditional pilot assistant scheme because it consumes no extra bandwidth for the training sequence is superimposed to the data sequence. The simulations demonstrate that the proposed estimator can approach the performance of the pilot assistant scheme very well in terms of MSE and BER with great bandwidth efficiency improvement. Overall, the proposed solution outperforms the traditional pilot assistant scheme.

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“…For single carrier transmissions, [6] proposes the use of time multiplexed pilots in a two-step algorithm; first, a coarse estimation of the CFO is made and subsequently a fine estimation is carried out. In the case of implicit training (training sequence superimposed onto data), CFO estimation and compensation was studied in [7][8][9].…”

Section: Introductionmentioning

confidence: 99%

Channel and carrier frequency offset estimation based on projection onto a bidimensional basis

Carrasco-Alvarez¹,

Parra-Michel²,

Orozco-Lugo³

et al. 2019

Turk J Elec Eng & Comp Sci

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Two of the most counterproductive effects that must be dealt with in communication systems in realistic environments are carrier frequency offset (CFO) and time-varying channels. These problems are usually addressed by using independent approaches for each one. This paper introduces an algorithm that attacks both of these effects in a joint fashion. It is based on a rough compensation of CFO, and after considering that the remaining CFO uncertainty can be seen as part of the time-varying channel a channel estimation that includes that composite channel is performed. Particularly, the channel estimation based on projection onto a bidimensional basis is shown to be adequate to perform this process. The approach suggested in this paper is illustrated for a superimposed training-based communication system. Simulation results corroborate the validity of the proposed algorithm, where the performance obtained is similar to that achieved for the time-varying channel estimators based on bidimensional basis projection when no CFO is present.

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Carrier Frequency Offset Estimation using Data-Dependent Superimposed Training

Cited by 5 publications

References 9 publications

Linearly time-varying channel estimation and training power allocation for OFDM/MIMO systems using superimposed training

Linearly time-varying channel estimation and training power allocation for OFDM/MIMO systems using superimposed training

Carrier frequency offset estimation based on data-dependent superimposed training for OFDM systems

Channel and carrier frequency offset estimation based on projection onto a bidimensional basis

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