Time-varying channel estimation and symbol detection for OFDM systems using superimposed training

Zhang, H.; Dai, Xianhua

doi:10.1049/el:20071400

Cited by 6 publications

(6 citation statements)

References 4 publications

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“…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%

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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“…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%

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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“…Note that (1) reveals that the power spectra of h SR,l (t) and h RD,l (t) span over the bandwidths f SR = f S + f R and f RD = f R + f D , respectively, which indicates an increased Doppler effect for the mobile-to-mobile communications. Without loss of generality, perfect synchronization is assumed in this article as did in [7][8][9][10][11]13,[15][16][17][18][19]22].…”

Section: Problem Formulationmentioning

confidence: 99%

“…where i 2 {SR, RD} [15,18]. However, in high mobility environment, the extrapolation on the edge of subblocks generates unreliable channel estimates, which results in the severe performance degradation.…”

Section: Channel Smoothing Over One Ofdm Blockmentioning

confidence: 99%

“…To alleviate the number of unknown channel parameters, doubly selective channels are typically represented by two ways: by using the basis expansion model (BEM) [12][13][14], which decomposes the channel into a superposition of time-varying orthogonal basis functions (e.g., Fourier bases) weighted by time-invariant coefficients, and by using a blockwise linear model [15], which tracks the channel variation as a linear fashion over specific block periods. Previous contributions on channel estimation involving either BEM or blockwise linear channel models have been reported by the authors of [12,13,[15][16][17][18]. Although such channel modeling methods are generally reliable for a relatively high Doppler frequency, more than 30% transmission efficiency is wasted for transmitting known pilots, thus leading to a reduction in transmission efficiency.…”

Section: Introductionmentioning

confidence: 99%

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Doubly selective channel estimation for OFDM modulated amplify-and-forward relay networks using superimposed training

Zhang

Leung

Gao

et al. 2012

J Wireless Com Network

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This article is concerned with the problem of superimposed training (ST)-aided channel estimation for orthogonal frequency division multiplexingmodulated amplify-and-forwardrelay networks in doubly selective environment. A 'subblockwise' linear assumption-based channel model is proposed to represent the mobile-to-mobile time-and frequency-selective channels. We then propose a novel ST strategy that allows the destination node to separately obtain the channel information of the source ! relay link and the relay ! destination link, from which the optimal ST signals are derived by minimizing the channel mean-square-error. To enhance the performance of channel estimation, a subblock tracking-based low-complexity decision feedback approach is introduced to iteratively mitigate the unknown data interference. Finally, extensive numerical results are provided to corroborate the proposed studies.

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“…Then, we focus on ST design. From (14), when the training sequence at each pilot cluster is designed as either a constant modulus sequence, i.e.…”

Section: B Tmentioning

confidence: 99%

Superimposed Training-Aided Channel Estimation for Multiple Input Multiple Output-Orthogonal Frequency Division Multiplexing Systems over High-Mobility Environment

Zhang¹,

Dai²,

Pan³

et al. 2012

Wireless Communications and Networks - Recent Advances

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Time-varying channel estimation and symbol detection for OFDM systems using superimposed training

Cited by 6 publications

References 4 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

Doubly selective channel estimation for OFDM modulated amplify-and-forward relay networks using superimposed training

Superimposed Training-Aided Channel Estimation for Multiple Input Multiple Output-Orthogonal Frequency Division Multiplexing Systems over High-Mobility Environment

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