673 research outputs found
Channel, Phase Noise, and Frequency Offset in OFDM Systems: Joint Estimation, Data Detection, and Hybrid Cramer-Rao Lower Bound
Oscillator phase noise (PHN) and carrier frequency offset (CFO) can adversely
impact the performance of orthogonal frequency division multiplexing (OFDM)
systems, since they can result in inter carrier interference and rotation of
the signal constellation. In this paper, we propose an expectation conditional
maximization (ECM) based algorithm for joint estimation of channel, PHN, and
CFO in OFDM systems. We present the signal model for the estimation problem and
derive the hybrid Cramer-Rao lower bound (HCRB) for the joint estimation
problem. Next, we propose an iterative receiver based on an extended Kalman
filter for joint data detection and PHN tracking. Numerical results show that,
compared to existing algorithms, the performance of the proposed ECM-based
estimator is closer to the derived HCRB and outperforms the existing estimation
algorithms at moderate-to-high signal-to-noise ratio (SNR). In addition, the
combined estimation algorithm and iterative receiver are more computationally
efficient than existing algorithms and result in improved average uncoded and
coded bit error rate (BER) performance
Coded DS-CDMA Systems with Iterative Channel Estimation and no Pilot Symbols
In this paper, we describe direct-sequence code-division multiple-access
(DS-CDMA) systems with quadriphase-shift keying in which channel estimation,
coherent demodulation, and decoding are iteratively performed without the use
of any training or pilot symbols. An expectation-maximization
channel-estimation algorithm for the fading amplitude, phase, and the
interference power spectral density (PSD) due to the combined interference and
thermal noise is proposed for DS-CDMA systems with irregular repeat-accumulate
codes. After initial estimates of the fading amplitude, phase, and interference
PSD are obtained from the received symbols, subsequent values of these
parameters are iteratively updated by using the soft feedback from the channel
decoder. The updated estimates are combined with the received symbols and
iteratively passed to the decoder. The elimination of pilot symbols simplifies
the system design and allows either an enhanced information throughput, an
improved bit error rate, or greater spectral efficiency. The interference-PSD
estimation enables DS-CDMA systems to significantly suppress interference.Comment: To appear, IEEE Transactions on Wireless Communication
Preamble-Based Channel Estimation for CP-OFDM and OFDM/OQAM Systems: A Comparative Study
In this paper, preamble-based least squares (LS) channel estimation in OFDM
systems of the QAM and offset QAM (OQAM) types is considered, in both the
frequency and the time domains. The construction of optimal (in the mean
squared error (MSE) sense) preambles is investigated, for both the cases of
full (all tones carrying pilot symbols) and sparse (a subset of pilot tones,
surrounded by nulls or data) preambles. The two OFDM systems are compared for
the same transmit power, which, for cyclic prefix (CP) based OFDM/QAM, also
includes the power spent for CP transmission. OFDM/OQAM, with a sparse preamble
consisting of equipowered and equispaced pilots embedded in zeros, turns out to
perform at least as well as CP-OFDM. Simulations results are presented that
verify the analysis
Semiblind iterative data detection for OFDM systems with CFO and doubly selective channels
Data detection for OFDM systems over unknown doubly selective channels (DSCs) and carrier frequency offset (CFO) is investigated. A semiblind iterative detection algorithm is developed based on the expectation-maximization (EM) algorithm. It iteratively estimates the CFO, channel and recovers the unknown data using only limited number of pilot subcarriers in one OFDM symbol. In addition, efficient initial CFO and channel estimates are also derived based on approximated maximum likelihood (ML) and minimum mean square error (MMSE) criteria respectively. Simulation results show that the proposed data detection algorithm converges in a few iterations and moreover, its performance is close to the ideal case with perfect CFO and channel state information. © 2010 IEEE.published_or_final_versio
Joint CFO Estimation and Data Detection in OFDM systems
Orthogonal frequency division multiplexing (OFDM) is a multicarrier modulation technique that is widely used in wireless broadband communication systems. The spectral e ciency of OFDM is very high since the subcarriers are spaced as closely as possible while maintaining orthogonality. However, one of the major problems with OFDM that can cause performance degradation is carrier frequency o set (CFO) which impairs the orthogonality among OFDM subcarriers, as a consequence, results in inter-subcarrier interference. In this thesis, an iterative algorithm for joint CFO estimation and data detection in OFDM systems over frequency selective channels is proposed. The proposed algorithm is performing both CFO estimation and data detection in the frequency domain based on the Expectation-Maximization (EM) algorithm. The proposed algorithm can achieve the same bit-error-rate (BER) performance as that of its time-domain counterpart with much lower complexity. Simulation results show that the proposed algorithm can converge after three iterations and an estimate of CFO can be obtained with high accuracy
Semi-blind CFO, channel estimation and data detection for ofdm systems over doubly selective channels
Proceedings of the IEEE International Symposium on Circuits and Systems, 2010, p. 1887-1890Semi-blind joint CFO, channel estimation and data detection for OFDM systems over doubly selective channels (DSCs) is investigated in this work. A joint iterative algorithm is developed based on the maximum a posteriori expectation-maximization (MAP-EM) algorithm. In addition, a novel algorithm is also proposed to obtain the initial estimates of CFO and channels. Simulation results show that the performance of the proposed CFO and channel estimators approaches to that of the estimators with full training at high SNRs. Moreover, after convergence, the performance of data detection is close to the ideal case with perfect CFO and channel state information. ©2010 IEEE.published_or_final_versionThe IEEE International Symposium on Circuits and Systems (ISCAS), Paris, France, 30 May-2 June 2010. In Proceedings of ISCAS, 2010, p. 1887-189
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