Adaptive Low-Complexity MMSE Channel Estimation for OFDM

Lowe, D.; Huang, Xiaojing

doi:10.1109/iscit.2006.339825

Cited by 5 publications

(5 citation statements)

References 6 publications

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“…For the symbol detection, we consider two different equalizer structures: the one-tap equalizer and an iterative structure based on ML decision rule given by (8) and (9), respectively. Due to its high computational cost, the iterative algorithm only evaluates a restricted group of data vectors that differ by one bit from the last estimate vector.…”

Section: A System Modelmentioning

confidence: 99%

“…Since linear MMSE based techniques (e.g., [8]- [12]) that only exploit the behavior of the linear portion of the channel are unlikely to perform well in the presence of nonlinear distortion, the goal of this work is to derive linear CEs that embody both the effect of the linear and nonlinear components of the communications channel usually present in RoF uplink systems. It is also important to highlight that it is a topic that is not well exploited in the literature.…”

Section: Introductionmentioning

confidence: 99%

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A New MSE Channel Estimator Optimized for Nonlinearly Distorted Faded OFDM Signals With Applications to Radio Over Fiber

Oliveira¹,

Salgado

Rodrigues³

2014

IEEE Trans. Commun.

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In this paper, we propose a new channel estimation (CE) structure that aims to minimize the mean square error (MMSE) of orthogonal frequency-division multiplexing signals that are corrupted by fading, nonlinear, and additive white Gaussian noise channels. We demonstrate that it exhibits a simple structure consisting of a set of correlators matched to every frequency component in the nonlinearly distorted signal, followed by a linear transformation. An extension suitable for frequency shifted signals is also discussed. As case study, the potential of such CE in uplink radio-over-fiber (RoF) systems conveying WiFi signals is analyzed. Through simulations, we demonstrate the usage of the one-tap and iterative maximum likelihood (ML)-algorithm-based equalizers, when used jointly with the proposed MMSE, zero forcing, or perfect CEs. Results show that the performance of the MMSE CE approximates the one of the perfect CE. In addition, when used in conjunction with the ML algorithm, it enables relaxation of linear requirements of components in RoF by passing the need for transmitter predistortion techniques that would otherwise significantly increase wireless devices complexity.Index Terms-OFDM, fading, mean square error methods, optical fiber communication, nonlinear distortion, maximum likelihood detection.

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Section: A System Modelmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A New MSE Channel Estimator Optimized for Nonlinearly Distorted Faded OFDM Signals With Applications to Radio Over Fiber

Oliveira¹,

Salgado

Rodrigues³

2014

IEEE Trans. Commun.

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“…The channel estimate ĥ can be obtained using the zero forcing (ZF) or the minimum mean-square error (MMSE) channel estimator [14][15][16]. For example, by using the ZF channel estimator, the channel estimate vector ĥ is given by…”

Section: Channel Estimation In Mobile Wimaxmentioning

confidence: 99%

“…In order to improve the quality of the channel estimate, an MMSE based channel estimator [14,16] in the frequency domain is given by [14]   …”

Section: Channel Estimation In Mobile Wimaxmentioning

confidence: 99%

Mobile WiMAX: Impact of channel estimation error on the performance of limited feedback linear precoding

Tran

Nix

Doufexi

2010

21st Annual IEEE International Symposium on Personal, Indoor and Mobile Radio Communications

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The mobile WiMAX standard (802.16e) uses multiple-input multiple-output (MIMO) limited feedback linear precoding to exploit the channel state information at the transmitter. Although the performance of limited feedback linear precoding in relation to traditional open-loop MIMO-OFDM has been extensively studied in the literature, these studies commonly assume perfect channel estimation at the receiver. In a practical OFDM-based system, the estimated channel matrix often differs from the actual channel matrix due to errors incurred in the channel estimation process. This results in degraded performance relative to the case with perfect channel estimation. To date, few researchers have studied the impact of channel estimation error on the performance of an OFDM limited feedback linear precoding system. This paper investigates the channel estimation error using 1) an MMSE channel estimator that takes into account the subcarrier correlation when estimating the channel, 2) a Low Rank (LR) channel estimator that relaxes the requirement for a perfect channel covariance matrix in the MMSE receiver, and 3) a ZF estimator where this correlation information is ignored. Simulation results show that with the MMSE estimator the system suffers very little array gain loss with a performance degradation of 0.2dB SNR. Compared to the MMSE estimator, the LR estimator incurs a small performance loss of around 0.5dB. Finally, when the ZF estimator is implemented, a significant performance degradation is observed with approximately 4-5dB loss in array gain loss.Index Terms-802.16e, WiMAX, MIMO, linear precoding, limited feedback.

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“…As a consequence, the frequency response of the channel is considered to be flat over each subcarrier. Therefore, channel estimation and equalization can be performed by simple zero-forcing (ZF) techniques [7] [8]. However, ZF estimators do not take into account the correlation between subcarriers from multipath and nonlinear distortion.…”

Section: Channel Estimation With Ofdm Signalling In Radio-over-fiber mentioning

confidence: 99%

Signal processing techniques for transmission impairments compensation in optical systems

Oliveira

Pessoa

Salgado

et al. 2010

2010 12th International Conference on Transparent Optical Networks

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Adaptive Low-Complexity MMSE Channel Estimation for OFDM

Cited by 5 publications

References 6 publications

A New MSE Channel Estimator Optimized for Nonlinearly Distorted Faded OFDM Signals With Applications to Radio Over Fiber

A New MSE Channel Estimator Optimized for Nonlinearly Distorted Faded OFDM Signals With Applications to Radio Over Fiber

Mobile WiMAX: Impact of channel estimation error on the performance of limited feedback linear precoding

Signal processing techniques for transmission impairments compensation in optical systems

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