1,258 research outputs found
Iterative joint frequency offset and channel estimation for OFDM systems using first and second order approximation algorithms
[[abstract]]To implement an algorithm for joint estimation of carrier frequency offset (CFO) and channel impulse response (CIR) in orthogonal frequency division multiplexing (OFDM) systems, the maximum-likelihood criterion is commonly adopted. A major difficulty arises from the highly nonlinear nature of the log-likelihood function which renders local extrema or multiple solutions for the CFO and CIR estimators. Use of an approximation method coupled with an adaptive iteration algorithm has been a popular approach to ease problem solving. The approximation used in those existing methods is usually of the first order level. Here, in addition to a new first order approximation method, we also propose a second order approximation method. Further, for the part of the adaptive iteration algorithm, we adopt a new technique which will enable performance improvement. Our first order approximation method is found to outperform the existing ones in terms of estimation accuracies, tracking range, computation complexity, and convergence speed. As expected, our second order approximation method provides an even further improvement at the expense of higher computation complication.[[notice]]補正完畢[[journaltype]]國外[[incitationindex]]SCI[[booktype]]紙本[[booktype]]電子版[[countrycodes]]DE
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
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.ARC Discovery Projects Grant DP14010113
Constrained Phase Noise Estimation in OFDM Using Scattered Pilots Without Decision Feedback
In this paper, we consider an OFDM radio link corrupted by oscillator phase
noise in the receiver, namely the problem of estimating and compensating for
the impairment. To lessen the computational burden and delay incurred onto the
receiver, we estimate phase noise using only scattered pilot subcarriers, i.e.,
no tentative symbol decisions are used in obtaining and improving the phase
noise estimate. In particular, the phase noise estimation problem is posed as
an unconstrained optimization problem whose minimizer suffers from the
so-called amplitude and phase estimation error. These errors arise due to
receiver noise, estimation from limited scattered pilot subcarriers and
estimation using a dimensionality reduction model. It is empirically shown
that, at high signal-to-noise-ratios, the phase estimation error is small. To
reduce the amplitude estimation error, we restrict the minimizer to be drawn
from the so-called phase noise geometry set when minimizing the cost function.
The resulting optimization problem is a non-convex program. However, using the
S-procedure for quadratic equalities, we show that the optimal solution can be
obtained by solving the convex dual problem. We also consider a less complex
heuristic scheme that achieves the same objective of restricting the minimizer
to the phase noise geometry set. Through simulations, we demonstrate improved
coded bit-error-rate and phase noise estimation error performance when
enforcing the phase noise geometry. For example, at high
signal-to-noise-ratios, the probability density function of the phase noise
estimation error exhibits thinner tails which results in lower bit-error-rate
A BP-MF-EP Based Iterative Receiver for Joint Phase Noise Estimation, Equalization and Decoding
In this work, with combined belief propagation (BP), mean field (MF) and
expectation propagation (EP), an iterative receiver is designed for joint phase
noise (PN) estimation, equalization and decoding in a coded communication
system. The presence of the PN results in a nonlinear observation model.
Conventionally, the nonlinear model is directly linearized by using the
first-order Taylor approximation, e.g., in the state-of-the-art soft-input
extended Kalman smoothing approach (soft-in EKS). In this work, MF is used to
handle the factor due to the nonlinear model, and a second-order Taylor
approximation is used to achieve Gaussian approximation to the MF messages,
which is crucial to the low-complexity implementation of the receiver with BP
and EP. It turns out that our approximation is more effective than the direct
linearization in the soft-in EKS with similar complexity, leading to
significant performance improvement as demonstrated by simulation results.Comment: 5 pages, 3 figures, Resubmitted to IEEE Signal Processing Letter
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