SNR Analysis of OFDM Systems in the Presence of Carrier Frequency Offset for Fading Channels

Lee, J.; Lou, Hui‐Ling; Toumpakaris, Dimitris; Cioffi, J.M.

doi:10.1109/twc.2006.256956

Cited by 62 publications

(29 citation statements)

References 10 publications

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“…The integer part of the normalized CFO introduces a cyclic shift of Y [m] [4]. In this paper the normalized CFO for each user is assumed to consist of only the fractional part and so | k | < 0.5.…”

Section: Signal Model For Ofdma Uplinkmentioning

confidence: 99%

“…CFO and STO also degrades the quality of the channel estimates. Errors in timing, frequency and channel estimation ensue significant losses in the effective signal-to-noise ratio (SNR) at the receiver increasing the error probability of the system [4]. Time and frequency synchronization along with channel estimation must be performed for every user in the OFDMA system since the CFO, STO, and CIR of each user is unique.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Compressed sensing technique for synchronization and channel estimation in OFDMA uplink transmissions

Ssenyuva¹,

Kurt

Anarım

2017

2017 25th European Signal Processing Conference (EUSIPCO)

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Abstract-In this study joint estimation of the symbol timing offset (STO), and channel impulse response (CIR) of each active user under the presence of the carrier frequency offset (CFO) is considered for the uplink of an orthogonal frequency-division multiple access (OFDMA) system. A new method based on the compressed sensing (CS) framework using pilot symbols is proposed for the joint estimation of the STO and the CIR of each active user. Sparsity is achieved through incorporating the STO, the cyclic prefix (CP) samples, and the CIR coefficients into a new signal model. The proposed method does not require CIR coefficients to be sparse. Numerical results of the performance of the proposed method using the orthogonal matching pursuit (OMP) algorithm is presented. The presence of CTO is also considered as a perturbation to the CS dictionary of pilot symbols.

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Section: Signal Model For Ofdma Uplinkmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Compressed sensing technique for synchronization and channel estimation in OFDMA uplink transmissions

Ssenyuva¹,

Kurt

Anarım

2017

2017 25th European Signal Processing Conference (EUSIPCO)

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“…When the relative frequency offset is greater than | | ≥ 0.5, the frequency offset introduces a cyclic shift of Y m [k] [16]. When | | ≥ 0.5, the integer frequency offset correction algorithms such as [17,18] are employed.…”

Section: System Modelmentioning

confidence: 99%

Compressive sensing based joint frequency offset and channel estimation for OFDM

Senyuva

Kurt

Anarım

2016

J Wireless Com Network

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We consider joint estimation of carrier frequency offset (CFO) and channel impulse response (CIR) for orthogonal frequency division multiplexing (OFDM) with pilot symbols. A new method based on compressed sensing is proposed. It has been shown that the CIR can be represented as a 1-block sparse signal by using a dictionary constructed by concatenating subspaces of CFO values taken from a search space. Recovery of both CFO and CIR is accomplished by the block orthogonal matching pursuit algorithm. The proposed method uses only one OFDM training block and does not require any initialization. The performance of the proposed method is compared against the well-established pilot based estimators: Moose, Classen, the maximum likelihood estimator, and the p-algorithm. Numerical results show that the performance of the proposed method does not depend on the value of the CFO. We also give worst-case upper bounds for the mean squared error of the CIR estimate for a sparse multipath channel.

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“…Mitigation of inter-carrier interference (ICI) caused by very high mobile velocities is out of the scope of this work. The remaining frequency offset after frequency synchronization may be modeled with increased noise at the receiver [21]. Furthermore, the algorithms considered in this paper are not among the most suitable algorithms for very high velocities where the ICI would be the most dominant.…”

Section: Performance Comparisonmentioning

confidence: 99%

Decision-Directed Channel Estimation Implementation for Spectral Efficiency Improvement in Mobile MIMO-OFDM

Ketonen

Juntti

Ylioinas

et al. 2013

J Sign Process Syst

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Channel estimation algorithms and their implementations for mobile receivers are considered in this paper. The 3GPP long term evolution (LTE) based pilot structure is used as a benchmark in a multiple-input multipleoutput (MIMO) orthogonal frequency division multiplexing (OFDM) receiver. The decision directed (DD) spacealternating generalized expectation-maximization (SAGE) algorithm is used to improve the performance from that of the pilot symbol based least-squares (LS) channel estimator. The performance is improved with high user velocities, where the pilot symbol density is not sufficient. Minimum mean square error (MMSE) filtering is also used in estimating the channel in between pilot symbols. The pilot overhead can be reduced to a third of the LTE pilot overhead with DD channel estimation, obtaining a ten percent increase in data throughput. Complexity reduction and latency issues are considered in the architecture design. The pilot based LS, MMSE and the SAGE channel estimators are implemented with a high level synthesis tool, synthesized with the UMC 0.18 μm CMOS technology and the performance-complexity trade-offs are studied. The MMSE estimator improves the performance from the simple LS estimator with LTE pilot structure and has low power consumption. The SAGE estimator has high power consumption but can be used with reduced pilot density to increase the data rate.

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SNR Analysis of OFDM Systems in the Presence of Carrier Frequency Offset for Fading Channels

Cited by 62 publications

References 10 publications

Compressed sensing technique for synchronization and channel estimation in OFDMA uplink transmissions

Compressed sensing technique for synchronization and channel estimation in OFDMA uplink transmissions

Compressive sensing based joint frequency offset and channel estimation for OFDM

Decision-Directed Channel Estimation Implementation for Spectral Efficiency Improvement in Mobile MIMO-OFDM

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