Intercarrier interference due to phase noise in OFDM - estimation and suppression

Petrovic, D.; Rave, Wolfgang; Fettweis, Gerhard

doi:10.1109/vetecf.2004.1400429

Cited by 39 publications

(23 citation statements)

References 6 publications

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“…However, some of the work in [37,38,51] clearly demonstrate that this does not hold even when the number of subcarriers is large. This is because the ICI is mainly composed of interference from the neighboring subcarriers because typical phase noise processes are low-pass processes and practically zero interference occurs from far away subcarriers.…”

Section: Performance Analysismentioning

confidence: 99%

Performance Analysis of OFDM with Wiener Phase Noise and Frequency Selective Fading Channel

Mathecken

Riihonen

Werner

et al. 2011

IEEE Trans. Commun.

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Instructor:Taneli Riihonen, M.Sc. (Tech.) This thesis studies the effect of Wiener phase noise on the performance of orthogonal frequency division multiplexing (OFDM) systems. The main performance metrics used in the analysis are capacity and signal-to-interference-plus-noise ratio (SINR).OFDM is a multi-carrier modulation technique in which data is transmitted in parallel streams using closely spaced (in frequency) orthogonal carriers. Phase noise is the random fluctuation in the phase of the oscillator signal used in the frequency translation between baseband and radio frequency. These fluctuations occur because of the inherent imperfections in the components that make up the oscillator. With respect to OFDM, phase noise destroys the orthogonality between the carriers and this causes interference between the parallel streams of data which results in degradation of the capacity and SINR. We derive closed-form analytical expressions of average capacity and average SINR and highlight the key parameters of the phase noise process and OFDM system that affect its behavior. In comparison with previous works, a probability density function (PDF) based approach is used in arriving at these performance metrics. This approach necessitates the derivation of the PDF of a sum of gamma random variables. In earlier literature, this result is available for gamma variables that have a full-rank square-root normalized covariance matrix. We generalize the result for the rank-deficient case and apply this result to obtain the statistical expressions of capacity and SINR.

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Section: Performance Analysismentioning

confidence: 99%

Performance Analysis of OFDM with Wiener Phase Noise and Frequency Selective Fading Channel

Mathecken

Riihonen

Werner

et al. 2011

IEEE Trans. Commun.

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“…zero mean Gaussian variable with variance σ 2 v = 2πβT /N with βT the PHN rate. As a consequence, using (4) and (5) and assuming the initial condition φ −1 = 0 due to perfect phase synchronization at the beginning of the OFDM symbol as in [2], [6], a discrete recursive relation for phase distortions including both PHN and CFO can be summarized as :…”

Section: B Phase Distortion Modelmentioning

confidence: 99%

Monte Carlo Methods for Channel, Phase Noise, and Frequency Offset Estimation With Unknown Noise Variances in OFDM Systems

Septier

Delignon²,

Menhaj-Rivenq

et al. 2008

IEEE Trans. Signal Process.

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In this paper, we address the problem of OFDM channel estimation in the presence of phase noise (PHN) and carrier frequency offset (CFO). In OFDM systems, PHN and CFO cause two effects: the common phase error (CPE) and the intercarrier interference (ICI) which severely degrade the accuracy of the channel estimate. In literature, several algorithms have been proposed to solve this problem.Nevertheless, in all these existing schemes, both the PHN and the Additive White Gaussian Noise (AWGN) powers are assumed to be known. Because no a priori knowledge of PHN and AWGN powers is available at the receiver, we propose different strategies for the estimation of channel impulse response (CIR), CFO, PHN and also the PHN and the AWGN powers. Based on Monte Carlo methods, the proposed approaches estimate these many unknowns in the time domain from a training OFDM symbol using either off-line or on-line estimators. In the on-line case, we propose Sequential Monte Carlo algorithms and especially an original maximization step of the joint a posteriori probability density function for the unknown parameters. Simulation results are provided to illustrate the efficiency of the proposed algorithms in terms of mean square error (MSE) on channel, phase distortions and also noise power estimation. Index TermsOrthogonal frequency-division multiplexing (OFDM), channel estimation, phase noise, carrier frequency offset, sequential Monte-Carlo methods, Rao-blackwellization, optimal importance function, on-F. Septier and A. Menhaj-Rivencq are with the IEMN-DOAE UMR 8520, university of Valenciennes

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“…Since most energy of the frequency responses of PHN is concentrated in the neighborhood of the CPE g m,0 [3], we try to estimate the initial effective CIR vector η m,P(i) = g m,P(i) h m with the index set P = {P(0), · · · , P(2P)} where P = {P(i); 0 ≤ P(i) ≤ P or N − P ≤ P(i) < N} and P is the order of the estimation for the initial effective CIRs. Considering…”

Section: Joint Channel Estimation and Phase Noise Suppressionmentioning

confidence: 99%

“…The phase noise (PHN) induced by oscillator instability can cause the common phase error (CPE) and the intercarrier interference (ICI), which may result in significant performance degradation [2], [3]. Under the assumption that the channel estimation is perfect, several PHN mitigation schemes for OFDM systems are reported in [3], [4].…”

Section: Introductionmentioning

confidence: 99%

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Joint Channel Estimation and Phase Noise Suppression for OFDM Systems

Kim

2008

IEICE Transactions on Communications

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SUMMARYPhase noise (PHN) can cause the common phase error (CPE) and the inter-carrier interference (ICI), both of which impair the accurate channel estimation in orthogonal frequency division multiplexing (OFDM) systems. In this letter, we build a new signal model parameterized by the channel impulse response, the CPE and the ICI. Based on this model, we derive the maximum likelihood estimator (MLE) and the minimum mean square error estimator (MMSEE). Simulation results show that the proposed schemes significantly improve the performance of OFDM systems in the presence of PHN. key words: maximum-likelihood, minimum mean square error, orthogonal frequency-division multiplexing (OFDM), phase noise (PHN)

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Intercarrier interference due to phase noise in OFDM - estimation and suppression

Cited by 39 publications

References 6 publications

Performance Analysis of OFDM with Wiener Phase Noise and Frequency Selective Fading Channel

Performance Analysis of OFDM with Wiener Phase Noise and Frequency Selective Fading Channel

Monte Carlo Methods for Channel, Phase Noise, and Frequency Offset Estimation With Unknown Noise Variances in OFDM Systems

Joint Channel Estimation and Phase Noise Suppression for OFDM Systems

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