1,497 research outputs found
Estimation of Sparse MIMO Channels with Common Support
We consider the problem of estimating sparse communication channels in the
MIMO context. In small to medium bandwidth communications, as in the current
standards for OFDM and CDMA communication systems (with bandwidth up to 20
MHz), such channels are individually sparse and at the same time share a common
support set. Since the underlying physical channels are inherently
continuous-time, we propose a parametric sparse estimation technique based on
finite rate of innovation (FRI) principles. Parametric estimation is especially
relevant to MIMO communications as it allows for a robust estimation and
concise description of the channels. The core of the algorithm is a
generalization of conventional spectral estimation methods to multiple input
signals with common support. We show the application of our technique for
channel estimation in OFDM (uniformly/contiguous DFT pilots) and CDMA downlink
(Walsh-Hadamard coded schemes). In the presence of additive white Gaussian
noise, theoretical lower bounds on the estimation of SCS channel parameters in
Rayleigh fading conditions are derived. Finally, an analytical spatial channel
model is derived, and simulations on this model in the OFDM setting show the
symbol error rate (SER) is reduced by a factor 2 (0 dB of SNR) to 5 (high SNR)
compared to standard non-parametric methods - e.g. lowpass interpolation.Comment: 12 pages / 7 figures. Submitted to IEEE Transactions on Communicatio
Scattered Pilots and Virtual Carriers Based Frequency Offset Tracking for OFDM Systems: Algorithms, Identifiability, and Performance Analysis
In this paper, we propose a novel carrier frequency offset (CFO) tracking algorithm for orthogonal frequency division multiplexing (OFDM) systems by exploiting scattered pilot carriers and virtual carriers embedded in the existing OFDM standards. Assuming that the channel remains constant during two consecutive OFDM blocks and perfect timing, a CFO tracking algorithm is proposed using the limited number of pilot carriers in each OFDM block. Identifiability of this pilot based algorithm is fully discussed under the noise free environment, and a constellation rotation strategy is proposed to eliminate the c-ambiguity for arbitrary constellations. A weighted algorithm is then proposed by considering both scattered pilots and virtual carriers. We find that, the pilots increase the performance accuracy of the algorithm, while the virtual carriers reduce the chance of CFO outlier. Therefore, the proposed tracking algorithm is able to achieve full range CFO estimation, can be used before channel estimation, and could provide improved performance compared to existing algorithms. The asymptotic mean square error (MSE) of the proposed algorithm is derived and simulation results agree with the theoretical analysis
Eigenvector-based multidimensional frequency estimation : identifiability, performance, and applications.
Multidimensional frequency estimation is a classic signal processing problem that has versatile applications in sensor array processing and wireless communications. Eigenvalue-based two-dimensional (2-D) and N -dimensional ( N -D) frequency estimation algorithms have been well documented, however, these algorithms suffer from limited identifiability and demanding computations. This dissertation develops a framework on eigenvector-based N -D frequency estimation, which contains several novel algorithms that estimate a structural matrix from eigenvectors and then resolve the N -D frequencies by dividing the elements of the structural matrix. Compared to the existing eigenvalue-based algorithms, these eigenvector-based algorithms can achieve automatic pairing without an extra frequency pairing step, and tins the computational complexity is reduced. The identifiability, performance, and complexity of these algorithms are also systematically studied. Based on this study, the most relaxed identifiability condition for the N -D frequency estimation problem is given and an effective approach using optimized weighting factors to improve the performance of frequency estimation is developed. These results are applied in wireless communication for time-varying multipath channel estimation and prediction, as well as for joint 2-D Direction-of-arrival (DOA) tracking of multiple moving targets
Phase-Noise Compensation for OFDM Systems Exploiting Coherence Bandwidth: Modeling, Algorithms, and Analysis
Phase-noise (PN) estimation and compensation are crucial in millimeter-wave
(mmWave) communication systems to achieve high reliability. The PN estimation,
however, suffers from high computational complexity due to its fundamental
characteristics, such as spectral spreading and fast-varying fluctuations. In
this paper, we propose a new framework for low-complexity PN compensation in
orthogonal frequency-division multiplexing systems. The proposed framework also
includes a pilot allocation strategy to minimize its overhead. The key ideas
are to exploit the coherence bandwidth of mmWave systems and to approximate the
actual PN spectrum with its dominant components, resulting in a non-iterative
solution by using linear minimum mean squared-error estimation. The proposed
method obtains a reduction of more than 2.5x in total complexity, as compared
to the existing methods. Furthermore, we derive closed-form expressions for
normalized mean squared-errors (NMSEs) as a function of critical system
parameters, which help in understanding the NMSE behavior in low and high
signal-to-noise ratio regimes. Lastly, we study a trade-off between performance
and pilot-overhead to provide insight into an appropriate approximation of the
PN spectrum.Comment: To appear in IEEE Transactions on Wireless Communication
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