DFT-based channel estimation in 2D-pilot-symbol-aided OFDM wireless systems

García, M. Julia Fernández‐Getino; Pàez-Borrallo, J.M.; Zazo, Santiago

doi:10.1109/vetecs.2001.944491

Cited by 60 publications

(25 citation statements)

References 10 publications

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“…This guard time includes the cyclically extended part of the OFDM symbol in order to preserve orthogonality and eliminate inter-carrier interference (ICI). It is well known that if the channel impulse response has a maximum of resolvable paths, then the GI must be at least equal to [1].…”

Section: Ofdm System Modelmentioning

confidence: 99%

SIMO-OFDM Channel Estimation based on Nonlinear Complex LS-SVM

Charrada¹,

Samet²

2012

IJCA

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In this contribution, we propose a robust highly selective nonlinear channel estimator for Single -Input Multiple-Output (SIMO) Orthogonal Frequency Division Multiplexing (OFDM) system using complex Least Squares Support Vector Machines (LS-SVM) and applied to Long Term Evolution (LTE) downlink under high mobility conditions .The new method uses the information provided by the pilot signals to estimate the total frequency response of the channel in two phases: learning phase and estimation phase. The estimation algorithm makes use of the reference signals to estimate the total frequency response of the highly selective multipath channel in the presence of non-Gaussian impulse noise interfering with pilot signals. Thus, the algorithm maps trained data into a high dimensional feature space and uses the Structural Risk Minimization (SRM) principle to carry out the regression estimation for the frequency response function of the highly selective channel. The simulations show the effectiveness of the proposed method which has good performance and high precision to track the variations of the fading channels compared to the conventional LS method and it is robust at high speed mobility.

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Section: Ofdm System Modelmentioning

confidence: 99%

SIMO-OFDM Channel Estimation based on Nonlinear Complex LS-SVM

Charrada¹,

Samet²

2012

IJCA

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“…Two common non-adaptive interpolators used in OFDM are a PolynomialBased Interpolator [10] and a Frequency Smoothing/DFT-based Estimator [5]. The…”

Section: Estimation/interpolationmentioning

confidence: 99%

Uplink Channel Estimation in WiMAX

Kwasinski

2009

2009 IEEE Wireless Communications and Networking Conference

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Over the last 20 years, we have seen a tremendous increase in demand for broadband internet access and cellular use. Many technological advances were made throughout this time period to increase internet access speeds and improve the voice service quality. But despite these advances, one of the current limitations in the current technology is that it cannot simultaneously provide both high-speed internet access equivalent to cable/DSL and coverage equivalent to the cellular networks.To overcome this limitation, WiMAX was developed to be the solution to this limitation. WiMAX is an emerging and powerful technology in broadband wireless communications in that it can provide high-speed broadband voice and data services over distances much greater than Wi-Fi.However, like in any wireless system, signal distortion due to channel fading, noise, and Doppler can limit the overall transmission data rate and coverage. To minimize the degradation in system performance caused by the channel, channel estimation must be performed to remove the effects of the channel. To aid the channel estimation process, known pilot subcarriers are embedded into each OFDM symbol and used by the receiver to measure the channel. Because the pilot arrangement depends on the mode or feature employed by WiMAX, different channel estimation algorithms must be developed to optimize performance.We first develop and present several channel estimation algorithms for the various modes and features supported by WiMAX. More specifically, we develop channel estimation algorithms that can be used in the PUSC and AMC subcarrier permutation and channel sounding feature. We then use analytical modeling and simulation to illustrate and analyze the performance of each algorithm under various channel conditions and compare them to an estimator with perfect channel knowledge. Our results show that channel delay spread and signal-to-noise ratio influence the performance of each algorithm and that some estimators perform better in certain channels and worse in others. With this knowledge, we can use a combination of our proposed algorithms and tune them according to the channel conditions to enhance the system's performance. By the end of this study, we will have a better understanding of the types of channels each algorithm performs best in and explain why each algorithm performs the way it does.

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“…Now, the OFDM system can be expressed as (2) where and are, respectively, the complex pilot or data symbol transmitted at the th subcarrier. It is well known that if the channel impulse response has a maximum of resolvable paths (and hence of degrees of freedom), then must be at least equal to [11].…”

Section: Ofdm Modelmentioning

confidence: 99%

“…In brief, the primal-dual functional is obtained by introducing the constraints into the primal functional by means of Lagrange multipliers , , , , for the real (subscript ) and imaginary (subscript ) parts of the residuals. By making zero the primal-dual functional gradient with respect to , we have the following expression for channel estimated values at pilot positions: (11) where . For notation, we define the following column vector: (12) and the following Gram matrix as .…”

Section: Ofdm-svm Coherent Demodulatormentioning

confidence: 99%

Support vector machines for robust channel estimation in OFDM

García

Rojo-Álvarez

Alonso‐Atienza

et al. 2006

IEEE Signal Process. Lett.

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Abstract-A new support vector machine (SVM) algorithm for coherent robust demodulation in orthogonal frequency-division multiplexing (OFDM) systems is proposed. We present a complex regression SVM formulation specifically adapted to a pilots-based OFDM signal. This novel proposal provides a simpler scheme than an SVM classification method. The feasibility of our approach is substantiated by computer simulation results obtained for IEEE 802.16 broadband fixed wireless channel models. These experiments allow to scrutinize the performance of the OFDM-SVM system and the suitability of the -Huber cost function, in the presence of non-Gaussian impulse noise interfering with OFDM pilot symbols.Index Terms-Channel estimation, complex, orthogonal frequency-division multiplexing (OFDM), robust estimation, support vector machine (SVM).

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DFT-based channel estimation in 2D-pilot-symbol-aided OFDM wireless systems

Cited by 60 publications

References 10 publications

SIMO-OFDM Channel Estimation based on Nonlinear Complex LS-SVM

SIMO-OFDM Channel Estimation based on Nonlinear Complex LS-SVM

Uplink Channel Estimation in WiMAX

Support vector machines for robust channel estimation in OFDM

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