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Qun Wan

Publications and source records attributed to Qun Wan.

At least 19 recordsLinked to original sources
arXiv2025

Generalized Tensor-Aided Channel Estimation for Hardware Impaired Device Identification

In this paper, we investigate the joint generalized channel estimation and device identification problem in Internet of Things (IoT) networks {under multipath propagation}. To fully utilize the received signal, we decompose the generalized channel into three components: transmitter hardware characteristics, path gains, and angles of arrival. By modelling the received signals as parallel factor (PARAFAC) tensors, we develop alternating least squares (ALS)-based algorithms to simultaneously estimate the generalized channels and identify the transmitters. Simulation results show that the proposed scheme outperforms {both Khatri-Rao Factorization (KRF) and the conventional least squares (LS) method} in terms of channel estimation accuracy and achieves performance close to the derived Cramer-Rao lower bound.

eess.SP↗
arXiv2021

A Computational Efficient Maximum Likelihood Direct Position Determination Approach for Multiple Emitters Using Angle and Doppler Measurements

Emitter localization is widely applied in the military and civilian _elds. In this paper, we tackle the problem of position estimation for multiple stationary emitters using Doppler frequency shifts and angles by moving receivers. The computational load for the exhaustive maximum likelihood (ML) direct position determination (DPD) search is insu_erable. Based on the Pincus' theorem and importance sampling (IS) concept, we propose a novel non-iterative ML DPD method. The proposed method transforms the original multidimensional grid search into random variables generation with multiple low-dimensional pseudo-probability density functions (PDF), and the circular mean is used for superior position estimation performance. The computational complexity of the proposed method is modest, and the o_-grid problem that most existing DPD techniques face is signi_cantly alleviated. Moreover, it can be implemented in parallel separately. Simulation results demonstrate that the proposed ML DPD estimator can achieve better estimation accuracy than state-of-the-art DPD techniques. With a reasonable parameter choice, the estimation performance of the proposed technique is very close to the Cram_er-Rao lower bound (CRLB), even in the adverse conditions of low signal-to-noise ratios (SNR) levels.

eess.SP↗
arXiv2021

Highly Efficient Ultrathin Light Emitting Diodes based on Perovskite Nanocrystals

Light-emitting diodes based on perovskite nanocrystals (PNCs-LEDs) have gained great interest for next-generation display and lighting technologies prized for their color purity, high brightness and luminous efficiency approaching the intrinsic limit imposed by extraction of electroluminescence from the device structure. Although the time is ripe for the development of effective light outcoupling strategies to further boost the device performance, this technologically relevant aspect of PNC-LEDs is still without a definitive solution. Here, following theoretical guidelines and without the integration of complex photonic structures, we realize stable PNC-LEDs with EQE as high as 29.2% (average EQE=24.7%), which substantially break the outcoupling limit of common PNC-LEDs and systematically surpass any previous perovskite-based device. Key to such unprecedented performance is channeling the recombination zone in PNC emissive layers as thin as 10 nm, which we achieve by finely balancing the electron and hole transport using CsPbBr3 PNCs resurfaced with a nickel oxide layer. The ultra-thin approach general and, in principle, applicable to other perovskite nanostructures for fabricating highly efficient, color tunable transparent LEDs ideal for unobtrusive screens and displays and is compatible with the integration of photonic components for further enhanced performance.

physics.optics↗
arXiv2014

A Novel Compressed Sensing Based Model for Reconstructing Sparse Signals Using Phase Sparse Character

Phase modulation is a commonly used modulation mode in digital communication, which usually brings phase sparsity to digital signals. It is naturally to connect the sparsity with the newly emerged theory of compressed sensing (CS), which enables sub-Nyquist sampling of high-bandwidth to sparse signals. For the present, applications of CS theory in communication field mainly focus on spectrum sensing, sparse channel estimation etc. Few of current researches take the phase sparse character into consideration. In this paper, we establish the novel model of phase modulation signals based on phase sparsity, and introduce CS theory to the phase domain. According to CS theory, rather than the bandwidth, the sampling rate required here is scaling with the symbol rate, which is usually much lower than the Nyquist rate. In this paper, we provide analytical support for the model, and simulations verify its validity.

cs.IT↗
arXiv2014

Automatic Modulation Recognition of PSK Signals Using Nonuniform Compressive Samples Based on High Order Statistics

Phase modulation is a commonly used modulation mode in digital communication, which usually brings phase sparsity to digital signals. It is naturally to connect the sparsity with the newly emerged theory of compressed sensing (CS), which enables sub-Nyquist sampling of high-bandwidth to sparse signals. For the present, applications of CS theory in communication field mainly focus on spectrum sensing, sparse channel estimation etc. Few of current researches take the phase sparse character into consideration. In this paper, we establish the novel model of phase modulation signals based on phase sparsity, and introduce CS theory to the phase domain. According to CS theory, rather than the bandwidth, the sampling rate required here is scaling with the symbol rate, which is usually much lower than the Nyquist rate. In this paper, we provide analytical support for the model, and simulations verify its validity.

cs.IT↗
arXiv2014

Automatic Modulation Recognition of PSK Signals with Sub-Nyquist Sampling Based on High Order Statistics

Sampling rate required in the Nth Power Nonlinear Transformation (NPT) method is typically much greater than Nyquist rate, which causes heavy burden for the Analog to Digital Converter (ADC). Taking advantage of the sparse property of PSK signals' spectrum under NPT, we develop the NPT method for PSK signals with Sub-Nyquist rate samples. In this paper, combined the NPT method with Compressive Sensing (CS) theory, frequency spectrum reconstruction of the Nth power nonlinear transformation of PSK signals is presented, which can be further used for AMR and rough estimations of unknown carrier frequency and symbol rate.

cs.IT↗
arXiv2013

Compressive Channel Estimation for Two-way Relay Network in a Frequency-Selective Channel with Compressed Sensing

Two-way relay network (TWRN) was introduced to realize high-data rate transmission over the wireless frequency-selective channel. However, TWRC requires the knowledge of channel state information (CSI) not only for coherent data detection but also for the self-data removal. This is partial accomplished by training sequence-based linear channel estimation. However, conventional linear estimation techniques neglect anticipated sparsity of multipath channel. Unlike the previous methods, we propose a compressive channel estimation method which exploit the sparse structure and provide significant improvements in MSE performance when compared with traditional LSbased linear channel probing strategies. Simulation results confirm the proposed methods.

cs.IT↗
arXiv2013

Sparse Channel Estimation for Amplify-and-Forward Two-way Relay Network with Compressed Sensing

Amplify-and-forward two-way relay network (AFTWRN) was introduced to realize high-data rate transmission over the wireless frequency-selective channel. However, AFTWRC requires the knowledge of channel state information (CSI) not only for coherent data detection but also for the selfdata removal. This is partial accomplished by training sequence-based linear channel estimation. However, conventional linear estimation techniques neglect anticipated sparsity of multipath channel and thus lead to low spectral efficiency which is scarce in the field of wireless communication. Unlike the previous methods, we propose a sparse channel estimation method which can exploit the sparse structure and hence provide significant improvements in MSE performance when compared with traditional LS-based linear channel probing strategies in AF-TWRN. Simulation results confirm the proposed methods.

cs.IT↗
arXiv2012

Strongly Convex Programming for Principal Component Pursuit

In this paper, we address strongly convex programming for princi- pal component pursuit with reduced linear measurements, which decomposes a superposition of a low-rank matrix and a sparse matrix from a small set of linear measurements. We first provide sufficient conditions under which the strongly convex models lead to the exact low-rank and sparse matrix recov- ery; Second, we also give suggestions on how to choose suitable parameters in practical algorithms.

cs.IT↗
arXiv2012

Complex Orthogonal Matching Pursuit and Its Exact Recovery Conditions

In this paper, we present new results on using orthogonal matching pursuit (OMP), to solve the sparse approximation problem over redundant dictionaries for complex cases (i.e., complex measurement vector, complex dictionary and complex additive white Gaussian noise (CAWGN)). A sufficient condition that OMP can recover the optimal representation of an exactly sparse signal in the complex cases is proposed both in noiseless and bound Gaussian noise settings. Similar to exact recovery condition (ERC) results in real cases, we extend them to complex case and derivate the corresponding ERC in the paper. It leverages this theory to show that OMP succeed for k-sparse signal from a class of complex dictionary. Besides, an application with geometrical theory of diffraction (GTD) model is presented for complex cases. Finally, simulation experiments illustrate the validity of the theoretical analysis.

cs.IT↗
arXiv2012

A Fast HRRP Synthesis Algorithm with Sensing Dictionary in GTD Model

To achieve high range resolution profile (HRRP), the geometric theory of diffraction (GTD) parametric model is widely used in stepped-frequency radar system. In the paper, a fast synthetic range profile algorithm, called orthogonal matching pursuit with sensing dictionary (OMP-SD), is proposed. It formulates the traditional HRRP synthetic to be a sparse approximation problem over redundant dictionary. As it employs a priori information that targets are sparsely distributed in the range space, the synthetic range profile (SRP) can be accomplished even in presence of data lost. Besides, the computational complexity is reduced by introducing sensing dictionary (SD) and it mitigates the model mismatch at the same time. The computation complexity decreases from O(MNDK) flops for OMP to O(M(N +D)K) flops for OMP-SD. Simulation experiments illustrate its advantages both in additive white Gaussian noise (AWGN) and noiseless situation, respectively.

cs.IT↗
arXiv2012

Enhanced Compressive Wideband Frequency Spectrum Sensing for Dynamic Spectrum Access

Wideband spectrum sensing detects the unused spectrum holes for dynamic spectrum access (DSA). Too high sampling rate is the main problem. Compressive sensing (CS) can reconstruct sparse signal with much fewer randomized samples than Nyquist sampling with high probability. Since survey shows that the monitored signal is sparse in frequency domain, CS can deal with the sampling burden. Random samples can be obtained by the analog-to-information converter. Signal recovery can be formulated as an L0 norm minimization and a linear measurement fitting constraint. In DSA, the static spectrum allocation of primary radios means the bounds between different types of primary radios are known in advance. To incorporate this a priori information, we divide the whole spectrum into subsections according to the spectrum allocation policy. In the new optimization model, the minimization of the L2 norm of each subsection is used to encourage the cluster distribution locally, while the L0 norm of the L2 norms is minimized to give sparse distribution globally. Because the L0/L2 optimization is not convex, an iteratively re-weighted L1/L2 optimization is proposed to approximate it. Simulations demonstrate the proposed method outperforms others in accuracy, denoising ability, etc.

cs.IT↗
arXiv2011

Indirect Channel Sensing for Cognitive Amplify-and-Forward Relay Networks

In cognitive radio network the primary channel information is beneficial. But it can not be obtained by direct channel estimation in cognitive system as pervious methods. And only one possible way is the primary receiver broadcasts the primary channel information to the cognitive users, but it would require the modification of the primary receiver and additional precious spectrum resource. Cooperative communication is also a promising technique. And this paper introduces an indirect channel sensing method for the primary channel in cognitive amplify-and-forward (AF) relay network. As the signal retransmitted from the primary AF relay node includes channel effects, the cognitive radio can receive retransmitted signal from AF node, and then extract the channel information from them. Afterwards, Least squares channel estimation and sparse channel estimation can be used to address the dense and sparse multipath channels respectively. Numerical experiment demonstrates that the proposed indirect channel sensing method has an acceptable performance.

cs.IT↗
arXiv2011

Sidelobe Suppression for Robust Beamformer via The Mixed Norm Constraint

Applying a sparse constraint on the beam pattern has been suggested to suppress the sidelobe of the minimum variance distortionless response (MVDR) beamformer recently. To further improve the performance, we add a mixed norm constraint on the beam pattern. It matches the beam pattern better and encourages dense distribution in mainlobe and sparse distribution in sidelobe. The obtained beamformer has a lower sidelobe level and deeper nulls for interference avoidance than the standard sparse constraint based beamformer. Simulation demonstrates that the SINR gain is considerable for its lower sidelobe level and deeper nulling for interference, while the robustness against the mismatch between the steering angle and the direction of arrival (DOA) of the desired signal, caused by imperfect estimation of DOA, is maintained too.

cs.IT↗
arXiv2011

Anti-Sampling-Distortion Compressive Wideband Spectrum Sensing for Cognitive Radio

Too high sampling rate is the bottleneck to wideband spectrum sensing for cognitive radio in mobile communication. Compressed sensing (CS) is introduced to transfer the sampling burden. The standard sparse signal recovery of CS does not consider the distortion in the analogue-to-information converter (AIC). To mitigate performance degeneration casued by the mismatch in least square distortionless constraint which doesn't consider the AIC distortion, we define the sparse signal with the sampling distortion as a bounded additive noise, and An anti-sampling-distortion constraint (ASDC) is deduced. Then we combine the \ell1 norm based sparse constraint with the ASDC to get a novel robust sparse signal recovery operator with sampling distortion. Numerical simulations demonstrate that the proposed method outperforms standard sparse wideband spectrum sensing in accuracy, denoising ability, etc.

cs.IT↗
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.

cs.IT↗