arXiv subjects

Qun Wan

Publications and source records attributed to Qun Wan.

29 records · Page 2Linked to original sources

arXiv2011

Anti-measurement Matrix Uncertainty Sparse Signal Recovery for Compressive Sensing

Compressive sensing (CS) is a technique for estimating a sparse signal from the random measurements and the measurement matrix. Traditional sparse signal recovery methods have seriously degeneration with the measurement matrix uncertainty (MMU). Here the MMU is modeled as a bounded additive error. An anti-uncertainty constraint in the form of a mixed L2 and L1 norm is deduced from the sparse signal model with MMU. Then we combine the sparse constraint with the anti-uncertainty constraint to get an anti-uncertainty sparse signal recovery operator. Numerical simulations demonstrate that the proposed operator has a better reconstructing performance with the MMU than traditional methods.

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arXiv2011

Total Variation Minimization Based Compressive Wideband Spectrum Sensing for Cognitive Radios

Wideband spectrum sensing is a critical component of a functioning cognitive radio system. Its major challenge is the too high sampling rate requirement. Compressive sensing (CS) promises to be able to deal with it. Nearly all the current CS based compressive wideband spectrum sensing methods exploit only the frequency sparsity to perform. Motivated by the achievement of a fast and robust detection of the wideband spectrum change, total variation mnimization is incorporated to exploit the temporal and frequency structure information to enhance the sparse level. As a sparser vector is obtained, the spectrum sensing period would be shorten and sensing accuracy would be enhanced. Both theoretical evaluation and numerical experiments can demonstrate the performance improvement.

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arXiv2010

Compressive Direction Finding Based on Amplitude Comparison

This paper exploits recent developments in compressive sensing (CS) to efficiently perform the direction finding via amplitude comprarison. The new method is proposed based on unimodal characteristic of antenna pattern and sparse property of received data. Unlike the conventional methods based peak-searching and symmetric constraint, the sparse reconstruction algorithm requires less pulse and takes advantage of CS. Simulation results validate the performance of the proposed method is better than the conventional methods.

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arXiv2010

Inter-atom Interference Mitigation for Sparse Signal Reconstruction Using Semi-blindly Weighted Minimum Variance Distortionless Response

The feasibility of sparse signal reconstruction depends heavily on the inter-atom interference of redundant dictionary. In this paper, a semi-blindly weighted minimum variance distortionless response (SBWMVDR) is proposed to mitigate the inter-atom interference. Examples of direction of arrival estimation are presented to show that the orthogonal match pursuit (OMP) based on SBWMVDR performs better than the ordinary OMP algorithm.

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arXiv2010

A Robust Beamformer Based on Weighted Sparse Constraint

Applying a sparse constraint on the beam pattern has been suggested to suppress the sidelobe level of a minimum variance distortionless response (MVDR) beamformer. In this letter, we introduce a weighted sparse constraint in the beamformer design to provide a lower sidelobe level and deeper nulls for interference avoidance, as compared with a conventional MVDR beamformer. The proposed beamformer also shows improved robustness against the mismatch between the steering angle and the direction of arrival (DOA) of the desired signal, caused by imperfect estimation of DOA.

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arXiv2010

Improved OMP Approach to Sparse Multi-path Channel Estimation via Adaptive Inter-atom Interference Mitigation

Since most components of sparse multi-path channel (SMPC) are zero, impulse response of SMPC can be recovered from a short training sequence. Though the ordinary orthogonal matching pursuit (OMP) algorithm provides a very fast implementation of SMPC estimation, it suffers from inter-atom interference (IAI), especially in the case of SMPC with a large delay spread and short training sequence. In this paper, an adaptive IAI mitigation method is proposed to improve the performance of SMPC estimation based on a general OMP algorithm. Unlike the ordinary OMP algorithm, a sensing dictionary is designed adaptively and posterior information is utilized efficiently to prevent false atoms from being selected due to serious IAI. Numeral experiments illustrate that the proposed general OMP algorithm based on adaptive IAI mitigation outperform both the ordinary OMP algorithm and the general OMP algorithm based on non-adaptive IAI mitigation.

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arXiv2010

A Fast Compressive Channel Estimation with Modified Smoothed L0 Algorithm

Broadband wireless channel is a time dispersive and becomes strongly frequency selective. In most cases, the channel is composed of a few dominant coefficients and a large part of coefficients is approximately zero or zero. To exploit the sparsity of multi-path channel (MPC), there are various methods have been proposed. They are, namely, greedy algorithms, iterative algorithms, and convex program. The former two algorithms are easy to be implemented but not stable; on the other hand, the last method is stable but difficult to be implemented as practical channel estimation problems because of computational complexity. In this paper, we proposed a novel channel estimation strategy by using modified smoothed (MSL0) algorithm which combines stable and low complexity. Computer simulations confirm the effectiveness of the introduced algorithm comparisons with the existing methods. We also give

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arXiv2010

Sparse Multipath Channel Estimation using DS Algorithm in Wideband Communication Systems

Wideband wireless channel is a time dispersive channel and becomes strongly frequency-selective. However, in most cases, the channel is composed of a few dominant taps and a large part of taps is approximately zero or zero. They are often called sparse multi-path channels (MPC). Conventional linear MPC methods, such as the least squares (LS), do not exploit the sparsity of MPC. In general, accurate sparse MPC estimator can be obtained by solving a LASSO problem even in the presence of noise. In this paper, a novel CS-based sparse MPC method by using Dantzig selector (DS) [1] is introduced. This method exploits a channel's sparsity to reduce the number of training sequence and, hence, increase spectral efficiency when compared to existed methods with computer simulations.

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arXiv2010

Sparse Multipath Channel Estimation Using Compressive Sampling Matching Pursuit Algorithm

Wideband wireless channel is a time dispersive channel and becomes strongly frequency-selective. However, in most cases, the channel is composed of a few dominant taps and a large part of taps is approximately zero or zero. To exploit the sparsity of multi-path channel (MPC), two methods have been proposed. They are, namely, greedy algorithm and convex program. Greedy algorithm is easy to be implemented but not stable; on the other hand, the convex program method is stable but difficult to be implemented as practical channel estimation problems. In this paper, we introduce a novel channel estimation strategy using compressive sampling matching pursuit (CoSaMP) algorithm which was proposed in [1]. This algorithm will combine the greedy algorithm with the convex program method. The effectiveness of the proposed algorithm will be confirmed through comparisons with the existing methods.

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arXiv2010

Power-Efficient Ultra-Wideband Waveform Design Considering Radio Channel Effects

This paper presents a power-efficient mask-constrained ultra-wideband (UWB) waveform design with radio channel effects taken into consideration. Based on a finite impulse response (FIR) filter, we develop a convex optimization model with respect to the autocorrelation of the filter coefficients to optimize the transmitted signal power spectrum, subject to a regulatory emission mask. To improve power efficiency, effects of transmitter radio frequency (RF) components are included in the optimization of the transmitter-output waveform, and radio propagation effects are considered for optimizing at the receiver. Optimum coefficients of the FIR filter are obtained through spectral factorization of their autocorrelations. Simulation results show that the proposed method is able to maximize the transmitted UWB signal power under mask constraints set by regulatory authorities, while mitigating the power loss caused by channel attenuations.

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arXiv2010

Compressive Wideband Spectrum Sensing for Fixed Frequency Spectrum Allocation

Too high sampling rate is the bottleneck to wideband spectrum sensing for cognitive radio (CR). As the survey shows that the sensed signal has a sparse representation in frequency domain in the mass, compressed sensing (CS) can be used to transfer the sampling burden to the digital signal processor. An analog to information converter (AIC) can randomly sample the received signal with sub-Nyquist rate to obtained the random measurements. Considering that the static frequency spectrum allocation of primary radios means the bounds between different primary radios is known in advance, here we incorporate information of the spectrum boundaries between different primary user as a priori information to obtain a mixed l2/l1 norm denoising operator (MNDO). In the MNDO, the estimated power spectrum density (PSD) vector is divided into block sections with bounds corresponding different allocated primary radios. Different from previous standard l1-norm constraint on the whole PSD vector, a sum of the l2 norm of each section of the PSD vector is minimized to encourage the local grouping distribution while the sparse distribution in mass, while a relaxed constraint is used to improve the denoising performance. Simulation demonstrates that the proposed method outperforms standard sparse spectrum estimation in accuracy, denoising ability, etc.

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