Conventional Orthogonal Matching Pursuit Research Articles

Deep Learning-Based Frequency-Selective Channel Estimation for Hybrid mmWave MIMO Systems

Millimeter wave (mmWave) massive multiple-input multiple-output (MIMO) systems typically employ hybrid mixed signal processing to avoid expensive hardware and high training overheads. However, the lack of fully digital beamforming at mmWave bands imposes additional challenges in channel estimation. Prior art on hybrid architectures has mainly focused on greedy optimization algorithms to estimate frequency-flat narrowband mmWave channels, despite the fact that in practice, the large bandwidth associated with mmWave channels results in frequency-selective channels. In this paper, we consider a frequency-selective wideband mmWave system and propose two deep learning (DL) compressive sensing (CS) based algorithms for channel estimation. The proposed algorithms learn critical apriori information from training data to provide highly accurate channel estimates with low training overhead. In the first approach, a DL-CS based algorithm simultaneously estimates the channel supports in the frequency domain, which are then used for channel reconstruction. The second approach exploits the estimated supports to apply a low-complexity multi-resolution fine-tuning method to further enhance the estimation performance. Simulation results demonstrate that the proposed DL-based schemes significantly outperform conventional orthogonal matching pursuit (OMP) techniques in terms of the normalized mean-squared error (NMSE), computational complexity, and spectral efficiency, particularly in the low signal-to-noise ratio regime. When compared to OMP approaches that achieve an NMSE gap of <inline-formula> <tex-math notation="LaTeX">$\mathrm {\{4-10\}\,\,dB}$ </tex-math></inline-formula> with respect to the Cramer Rao Lower Bound (CRLB), the proposed algorithms reduce the CRLB gap to only <inline-formula> <tex-math notation="LaTeX">$\mathrm {\{1-1.5\}\,\,dB}$ </tex-math></inline-formula>, while reducing complexity by two orders of magnitude.

Double Interpolation-Based Linear Fitting for OMP Channel Estimation in OFDM Systems

Conventional orthogonal matching pursuit (OMP) based channel estimation adopts discretized grids to represent path delays, which leads to energy leakage of off-grid paths. From the perspective of linear fitting, utilizing multiple atoms with fractional delays and shortening the distance between these atoms in the off-grid path reconstruction can reduce the energy leakage. To this end, an improved OMP channel estimation scheme is proposed in this letter for higher estimation accuracy. The proposed scheme utilizes trigonometric function based interpolation twice to estimate the delays of two atoms, which are adopted to reconstruct an off-grid path. Furthermore, a two-step search strategy is introduced to keep low computational complexity. The advantage of the proposed scheme is validated in simulations.

Parameter-Refined OMP for Compressive Radar Imaging of Rotating Targets

This paper presents a new parameter-refined orthogonal matching pursuit method to tackle the dictionary mismatch problem arising in compressive radar imaging of a rotating target with off-grid scatterers. The main idea is to replace the linear least-squares projection step of the conventional orthogonal matching pursuit (OMP) method with a nonlinear least-squares procedure which jointly estimates the off-grid positions and reflectivities of true scatterers. Simulations are presented to corroborate the performance advantages of the proposed method.

Sparse detection with orthogonal matching pursuit in multiuser uplink quadrature spatial modulation MIMO system

Quadrature spatial modulation (QSM) is one of the most prevalent transmission techniques for future wireless mobile network due to its high spectral efficiency (SE) and low complexity. However, it is restrictive to downlink of the cellular network deployments. Therefore, this paper proposes QSM for the multiuser uplink data transmission that increases the SE of the network by deploying multiple antennas at the mobile user with only two radio frequency (RF) chains. Maximum likelihood (ML) decoder is used to detect the received signal and attains the optimal detection performance but it is impractical because of its high computational complexity. Consequently, this paper proposes compressed sensing (CS) based orthogonal matching pursuit (OMP) detection as it has a suboptimal performance with a low computational complexity which can be a practical solution in the high-density uplink multiuser network. However, conventional OMP algorithm has a low estimation performance due to multiuser interference which corrupt channel matrix. Thus, this work design an equalizer that mitigates the multiuser interference and improve the detection performance by orthonormalizing the columns of channel matrix. Simulation analysis confirm that the proposed QSM technique based on CS detector outperforms the conventional schemes in terms of SE and bit error rate (BER).

Efficient Channel Estimation for mmWave MIMO With Transceiver Hardware Impairments

Transceiver hardware impairments (e.g., phase noise, high power amplifier nonlinearities, in-phase/quadrature-phase imbalance, and quantization errors) degrade the performance of channel estimation in millimeter wave (mmWave) multiple-input multiple-output (MIMO) systems. Although compensation methods can be exploited to mitigate the impact of hardware impairments, there always remains residual impairments that will distort the training pilots and received signals. In this paper, we reformulate the channel estimation with transceiver impairments into a sparse recovery problem from a Bayesian perspective and propose an efficient channel estimation algorithm. The proposed algorithm can effectively deal with the perturbation in channel estimation problem caused by the transmitter hardware impairments with small amount computation times. Simulation results demonstrate the superior performance of the proposed algorithm compared to the conventional orthogonal matching pursuit algorithm (OMP) based channel estimation method and Bayesian inference method.

A low-complexity orthogonal matching pursuit based channel estimation method for time-varying underwater acoustic OFDM systems

In this paper, we propose a low-complexity Orthogonal Matching Pursuit (OMP) based channel estimation method in underwater acoustic (UWA) orthogonal frequency division multiplexing (OFDM) system, where the channel is time-varying and described by a limited number of paths, each characterized by path delay, path gain and path Doppler scale. In the proposed method, we calculate the candidate path signature Hermitian inner product matrix in advance, avoiding the repeated calculations at each iteration in conventional OMP method. To further reduce the complexity, we estimate the path gains by solving a least-squares (LS) problem after the whole iteration procedure instead of doing this at each iteration step in the conventional OMP method. Results of numerical simulation and sea trial demonstrate the effectiveness of the proposed method in time-varying UWA channels, which can achieve the comparable performance with much lower computational complexity compared with the conventional OMP method.

Forward–backward processing for efficient underground channel estimation in 60 GHz MISO FBMC systems

In this Letter, a novel low complexity sparse channel estimation method based on forward–backward processing is proposed for mm-Wave underground channel incorporating a 2 × 1 space-time-block coding filter bank multi-carrier (STBC-FBMC) system, which employs quadrature amplitude modulation. The proposed estimation algorithm relies on the idea of the channel support forward selection and backward removal, which iteratively expands and compresses the support estimate, hence improving the ability to cover up for the errors made in the conventional orthogonal matching pursuit (OMP) algorithm. By contrast to the latter, the most innovative feature of the proposed approach is its capability of effective channel reconstruction without prior information of the sparsity level and its adaptive step size method to approach the true sparsity level. The proposed approach is shown to achieve a higher estimation accuracy than OMP alternative.

Compressive Sensing-Based Channel Estimation for FBMC-OQAM System Under Doubly Selective Channels

Filter-bank multicarrier with offset quadrature amplitude modulation (FBMC-OQAM) has been considered as an alternative scheme to orthogonal frequency division multiplexing (OFDM). However, the traditional channel estimation techniques of the OFDM cannot be directly applied to the FBMC-OQAM system because of the real-field orthogonality of the FBMC-OQAM signals. Although traditional channel estimation techniques, such as least square (LS) and minimum mean square error (MMSE), are widely applied to the FBMC-OQAM system via canceling the intrinsic imaginary interference from adjacent data symbols, the LS algorithm is subject to noise enhancement and it results in large mean square error (MSE), while the mmse algorithm needs to know the statistical information of channel in advance. Due to sparsity of the wireless channel, channel estimation is investigated as a compressive sensing (CS) problem. In this paper, we first introduce the coding method to cancel intrinsic imaginary interference for the FBMC-OQAM system. Then, a novel sparse adaptive subspace pursuit (SASP) method is proposed to improve the accuracy of LS channel estimation. Finally, we develop two distinctive algorithms, namely, auxiliary pilot (AP) SASP and coding-SASP, to estimate channel frequency respond (CFR) in the FBMC-OQAM system. The simulation results show that the AP-SASP and coding-SASP algorithms can offer a low complexity and fewer measurements compared with conventional orthogonal matching pursuit (OMP) and regularized OMP (ROMP) methods. Moreover, the proposed AP-SASP and coding-SASP algorithms have a better bit error ratio (BER) performance than the conventional OMP and ROMP methods for FBMC system in the doubly selective channels.

Distributed compressed sensing based channel estimation for underwater acoustic multiband transmissions.

Distributed compressed sensing techniques are applied to enhance sparse channel estimation performance in underwater acoustic multiband systems. The core idea is to use receptions from multiple sub-bands to enhance the detection of channel tap positions. A known variant of the orthogonal matching pursuit (OMP) algorithm based on the distributed compressed sensing principle is simultaneous orthogonal matching pursuit (SOMP). However, the impulse responses across multiple sub-bands may have different arrival structures, although they often show a certain level of similarity. To address such differences at the sub-bands, a multiple selection strategy is applied to select multiple candidates at individual sub-bands at each iteration. This is different from the conventional OMP and SOMP algorithms that select only one candidate at each iteration. When the multiple selection strategy is combined with the SOMP algorithm, the proposed algorithm is referred to as JB-MSSOMP algorithm. To take advantage of channel coherence between adjacent data blocks from different sub-bands, the multiple selection strategy is further used over time. This leads to JBT-MSSOMP algorithm. Computer simulations show improved channel estimation performance of the proposed JB-MSSOMP and JBT-MSSOMP algorithms over the OMP or SOMP algorithms. Communication data from a recent acoustic experiment demonstrates improved receiver performance with the proposed channel estimators.

Image super-resolution reconstruction based on regularization technique and guided filter

In order to improve the accuracy of sparse representation coefficients and the quality of reconstructed images, an improved image super-resolution algorithm based on sparse representation is presented. In the sparse coding stage, the autoregressive (AR) regularization and the non-local (NL) similarity regularization are introduced to improve the sparse coding objective function. A group of AR models which describe the image local structures are pre-learned from the training samples, and one or several suitable AR models can be adaptively selected for each image patch to regularize the solution space. Then, the image non-local redundancy is obtained by the NL similarity regularization to preserve edges. In the process of computing the sparse representation coefficients, the feature-sign search algorithm is utilized instead of the conventional orthogonal matching pursuit algorithm to improve the accuracy of the sparse coefficients. To restore image details further, a global error compensation model based on weighted guided filter is proposed to realize error compensation for the reconstructed images. Experimental results demonstrate that compared with Bicubic, L1SR, SISR, GR, ANR, NE+LS, NE+NNLS, NE+LLE and A+(16 atoms) methods, the proposed approach has remarkable improvement in peak signal-to-noise ratio, structural similarity and subjective visual perception.

Oblique Projection Matching Pursuit

Recent theory of compressed sensing (CS) tells us that sparse signals can be reconstructed from a small number of random samples. In reconstruction of sparse signals, greedy algorithms, such as the orthogonal matching pursuit (OMP), have been shown to be computationally efficient. In this paper, the performance of OMP is shown to be dependent on how well information of the underlying signals is preserved in the residual vector. Further, to improve the information preservation, we present a modification of OMP, called oblique projection matching pursuit (ObMP), which updates the residual in a oblique projection manor. Using the restricted isometric property (RIP), we build a solid yet very intuitive grasp of the more accurate phenomenon of ObMP. We also show from empirical experiments that the ObMP achieves improved reconstruction performance over the conventional OMP algorithm in terms of support detection ratio and mean squared error (MSE).

MMSE‐based version of OMP for recovery of discrete‐valued sparse signals

An enhanced algorithm for the reconstruction of discrete-valued sparse vectors from an underdetermined system of linear equations under the presence of noise is presented. Knowledge from the field of compressed sensing and from digital communications is combined in optimising the estimation step in the orthogonal matching pursuit (OMP) algorithm according to the minimum mean-squared error (MMSE) criterion. Thereby, a gain in performance of more than 1 dB is achieved compared with the conventional OMP.

다중사용자 공간변조시스템에서 압축센싱기반 신호복원 기법

In this paper, we propose a compressed sensing-based signal recovery technique for an uplink multi-user spatial modulation (MU-SM) system. In the MU-SM system, only one antenna among   antennas of each user becomes active by nature. Thus, this characteristics is exploited for signal recovery at a base station. We modify the conventional orthogonal matching pursuit (OMP) algorithm which has been widely used for sparse signal recovery in literature for the MU-SM system, which is called MU-OMP. We also propose a parallel OMP algorithm for the MU-SM system, which is called MU-POMP. Specifically, in the proposed algorithms, antenna indices of a specific user who was selected in the previous iteration are excluded in the next iteration of the OMP algorithm. Simulation results show that the proposed algorithms outperform the conventional OMP algorithm in the MU-SM system. 논문 14-39A-07-09 The Journal of Korea Information and Communications Society '14-07 Vol.39A No.07 http://dx.doi.org/10.7840/kics.2014.39A.7.424 424 ※ 본 연구는 2013년도 정부(교육부)의 재원으로 한국연구재단의 기초연구사업지원을 받아 수행된 것임(2013R1A1A2A10004905) First Author:Department of Information and Communication Engineering, Gyeongsang National University, jhpark.win@gnu.ac.kr, 학생회원 ° Corresponding Author : Department of Information and Communication Engineering & Institute of Marine Industry, Gyeongsang National University, bcjung@gnu.ac.kr, 종신회원 * Department of Information and Communication Engineering & Institute of Marine Industry, Gyeongsang National University, twban35@gnu.ac.kr, 종신회원 논문번호:KICS2014-05-211, Received May 31, 2014; Revised July 9, 2014; Accepted July 9, 2014 논문 /다중사용자 공간변조시스템에서 압축센싱기반 신호복원 기법

일반화된 공간천이변조 시스템에서 압축센싱기술을 이용한 수신신호 복호 알고리즘

In this paper, we propose a signal detection technique based on the parallel orthogonal matching pursuit (POMP) is proposed for generalized shift space keying (GSSK) systems, which is a modified version of the orthogonal matching pursuit (OMP) that is widely used as a greedy algorithm for sparse signal recovery. The signal recovery problem in the GSSK systems is similar to that in the compressed sensing (CS). In the proposed POMP technique, multiple indexes which have the maximum correlation between the received signal and the channel matrix are selected at the first iteration, while a single index is selected in the OMP algorithm. Finally, the index yielding the minimum residual between the received signal and the M recovered signals is selected as an estimate of the original transmitted signal. POMP with Quantization (POMP-Q) is also proposed, which combines the POMP technique with the signal quantization at each iteration. The proposed POMP technique induces the computational complexity M times, compared with the OMP, but the performance of the signal recovery significantly outperform the conventional OMP algorithm.

A robust compressive sensing based technique for reconstruction of sparse radar scenes

Pulse-Doppler radar has been successfully applied to surveillance and tracking of both moving and stationary targets. For efficient processing of radar returns, delay–Doppler plane is discretized and FFT techniques are employed to compute matched filter output on this discrete grid. However, for targets whose delay–Doppler values do not coincide with the computation grid, the detection performance degrades considerably. Especially for detecting strong and closely spaced targets this causes miss detections and false alarms. This phenomena is known as the off-grid problem. Although compressive sensing based techniques provide sparse and high resolution results at sub-Nyquist sampling rates, straightforward application of these techniques is significantly more sensitive to the off-grid problem. Here a novel parameter perturbation based sparse reconstruction technique is proposed for robust delay–Doppler radar processing even under the off-grid case. Although the perturbation idea is general and can be implemented in association with other greedy techniques, presently it is used within an orthogonal matching pursuit (OMP) framework. In the proposed technique, the selected dictionary parameters are perturbed towards directions to decrease the orthogonal residual norm. The obtained results show that accurate and sparse reconstructions can be obtained for off-grid multi target cases. A new performance metric based on Kullback–Leibler Divergence (KLD) is proposed to better characterize the error between actual and reconstructed parameter spaces. Increased performance with lower reconstruction errors are obtained for all the tested performance criteria for the proposed technique compared to conventional OMP and ℓ1 minimization techniques.

병렬OMP 기법을 통한 성긴신호 복원과 그 성능

본 논문에서는 성긴 신호의 복원을 위하여 기존의 직교매칭퍼슛 (orthogonal matching pursuit, OMP) 기술을 보완한 Parallel OMP (POMP) 기법을 제안하고 성능을 분석한다. POMP알고리즘의 과정은 간단하지만 기존 OMP와 비교하여 더 좋은 성능을 보이는 알고리즘이다. POMP 는 첫 번째 반복 과정에서 관찰 행렬과 상관도가 높은 인덱스 집합을 여러 개 선택한다. 선택된 각각의 인덱스를 첫 번째 인덱스로 하는 각각의 POMP 블록에서 OMP 알고리즘 기법이 병렬적으로 동작한다. 마지막으로 신호 복원을 위해 가장 작은 잔류 오차(residual)를 갖는 POMP블록의 인덱스 집합을 선택한다. 컴퓨터 시뮬레이션을 통해 제안된 POMP가 기존의 신호 복원 기술에 비하여 완벽복원비율과 평균 제곱 오차 (MSE) 측면에서 좋은 성능을 보임을 확인하였고, 이미지복원에 있어서는 눈으로 확인 가능할 정도의 성능 개선을 확인하였다. In this paper, parallel orthogonal matching pursuit (POMP) is proposed to supplement the orthogonal matching pursuit (OMP) which has been widely used as a greedy algorithm for sparse signal recovery. The process of POMP is simple but effective: (1) multiple indexes maximally correlated with the observation vector are chosen at the firest iteration, (2) the conventional OMP process is carried out in parallel for each selected index, (3) the index set which yields the minimum residual is selected for reconstructing the original sparse signal. Empirical simulations show that POMP outperforms than the existing sparse signal recovery algorithms in terms of exact recovery ratio (ERR) for sparse pattern and mean-squared error (MSE) between the estimated signal and the original signal.

High Resolution Direction of Arrival (DOA) Estimation Based on Improved Orthogonal Matching Pursuit (OMP) Algorithm by Iterative Local Searching

DOA (Direction of Arrival) estimation is a major problem in array signal processing applications. Recently, compressive sensing algorithms, including convex relaxation algorithms and greedy algorithms, have been recognized as a kind of novel DOA estimation algorithm. However, the success of these algorithms is limited by the RIP (Restricted Isometry Property) condition or the mutual coherence of measurement matrix. In the DOA estimation problem, the columns of measurement matrix are steering vectors corresponding to different DOAs. Thus, it violates the mutual coherence condition. The situation gets worse when there are two sources from two adjacent DOAs. In this paper, an algorithm based on OMP (Orthogonal Matching Pursuit), called ILS-OMP (Iterative Local Searching-Orthogonal Matching Pursuit), is proposed to improve DOA resolution by Iterative Local Searching. Firstly, the conventional OMP algorithm is used to obtain initial estimated DOAs. Then, in each iteration, a local searching process for every estimated DOA is utilized to find a new DOA in a given DOA set to further decrease the residual. Additionally, the estimated DOAs are updated by substituting the initial DOA with the new one. The simulation results demonstrate the advantages of the proposed algorithm.

Direction of arrival estimation using modified orthogonal matching pursuit algorithm

Direction of arrival (DOA) estimation is a sparse reconstruction problem. However, conventional orthogonal matching pursuit (OMP) may fail to identify the correct atoms since the redundant dictionary composed of the direction vectors is highly coherent. To mitigate the coherence problem, in this paper, we propose a modified OMP by constructing data dependent sensing dictionary for sparse reconstruction in the noisy case. Simulation results are presented to demonstrate the superior performance of the proposed algorithm for DOA estimation. Key words: Direction of arrival (DOA) estimation, orthogonal matching pursuit (OMP), sparse reconstruction, redundant dictionary, sensing dictionary.