A Bayesian State–Space Approach to Combat Inter-Carrier Interference in OFDM Systems

Palamides, Alex; Maras, A.M.

doi:10.1109/lsp.2007.898321

Cited by 16 publications

(7 citation statements)

References 5 publications

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“…We first develop the state-space model of the interest. Since the CFO is assumed to be a constant within each OFDM signal, the state equation of the CFO is built as [2] 1 n n…”

Section: State-space Modelmentioning

confidence: 99%

“…In the simulations, no prior knowledge is assumed for the estimation, i.e., the prior distribution ( ) p ε is the uniform distribution in (-0.5, 0.5). For performance comparison, EKF [2] and HF [3] are also applied to estimate the CFO.…”

Section: Simulationsmentioning

confidence: 99%

“…One such method is the Bayesian methodology. In [2], Palamides and Maras investigated two methods for estimating the CFO: the extended Kalman filter (EKF) and a form of particle filtering (PF) called sequential importance sampling. In [3], Jing et al proposed a CFO estimator based on extended H ∞ filter (HF).…”

Section: Introductionmentioning

confidence: 99%

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Frequency offset estimation in OFDM systems using Bayesian filtering

Yu¹

2011

SPIE Proceedings

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Orthogonal frequency division multiplexing (OFDM) is sensitive to carrier frequency offset (CFO) that causes intercarrier interference (ICI). In this paper, we present two schemes for the CFO estimation, which are based on rejection sampling (RS) and a form of particle filtering (PF) called kernel smoothing technique, respectively. The first scheme is offline estimation, where the observations contained in the OFDM training symbol are treated in the batch mode. The second scheme is online estimation, where the observations in the OFDM training symbol are treated in the sequential manner. Simulations are provided to illustrate the performances of the schemes. Performance comparisons of the two schemes and with other Bayesian methods are provided. Simulation results show that the two schemes are effective when estimating the CFO and can effectively combat the effect of ICI in OFDM systems.

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“…We first develop the state-space model of the interest. Since the CFO is assumed to be a constant within each OFDM signal, the state equation of the CFO is built as [2] 1 n n…”

Section: State-space Modelmentioning

confidence: 99%

Section: Simulationsmentioning

confidence: 99%

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Frequency offset estimation in OFDM systems using Bayesian filtering

Yu¹

2011

SPIE Proceedings

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“…A number of conventional approaches [1], the Kalman filter (KF) fi particle filter (PF) [2,3], etc. have been proposed to fight against here.…”

Section: Introductionmentioning

confidence: 99%

Pilot Tone based Gaussian Particle Filtering Approach for Carrier Frequency Offset Estimation in OFDM Systems

Ahmed¹,

Minal²

2015

IJCA

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Now these days OFDM is windy used for data communication. OFDM is used for transferring the high data rate. But there are some disadvantages of OFDM one of them is Carrier frequency offset. The cause behind the CFO is the synchronization between frequencies. It is a very sensitive so that hard to manage these frequencies. This paper presents a hybrid technique for carrier frequency offset (CFO) estimation in orthogonal frequency division multiplexing (OFDM) over frequency selective fading channel by using pilot tone and Gaussian particle filtering (PF) approach. An analytic expression in form of mean square error (MSE) of frequency offset synchronization is reported, and simulation results verify theoretical analysis and show approximate 97% improvement.

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“…Usually the problems of channel and frequency offset estimation are addressed separately. Most of the existing work for channel estimation in OFDM systems is done by assuming perfect carrier synchronization [2,1] and most of the existing work for frequency offset estimation is done under the assumption of known channel state information [9,13,10]. Joint estimation of channel state and carrier frequency offset (CFO) for OFDM systems was proposed in [7] and [3] but the estimation is based on the use of known training symbol sequences and/or virtual subcarriers.…”

Section: Introductionmentioning

confidence: 99%

Blind Sequential Monte Carlo Joint Tracking of Channel State and Frequency Offset in OFDM Systems

Palamides

Maras

2009

Wireless Pers Commun

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Orthogonal frequency division multiplexing (OFDM) is a multi-carrier modulation scheme, which has been adopted for several wireless standards. In order to fully exploit the benefits of an OFDM system, estimation of the channel state information must be performed. Moreover a well-known problem of OFDM is its sensitivity to frequency offset between the transmitted and received carrier frequencies. This frequency offset introduces inter-carrier interference in the OFDM signal. In this paper we address the problem of jointly tracking the channel and frequency offset based on a Sequential Monte Carlo filtering approach. The proposed algorithm works in a decision-directed way, thus does not require the use of pilot symbols, providing a worth-mentioned increase in the useful data rate. Through simulations we demonstrate the efficiency of this approach against a similar approach where the Extended Kalman Filter is used. Moreover our method is compared with two recently proposed pilot-based methods.

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A Bayesian State–Space Approach to Combat Inter-Carrier Interference in OFDM Systems

Cited by 16 publications

References 5 publications

Frequency offset estimation in OFDM systems using Bayesian filtering

Frequency offset estimation in OFDM systems using Bayesian filtering

Pilot Tone based Gaussian Particle Filtering Approach for Carrier Frequency Offset Estimation in OFDM Systems

Blind Sequential Monte Carlo Joint Tracking of Channel State and Frequency Offset in OFDM Systems

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