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14 pages, 1975 KiB

Open AccessArticle

An Improved SAMP Algorithm for Sparse Channel Estimation in OFDM System

by Hao Hu, Xu Zhao, Shiyong Chen and Tiancong Huang

Sensors 2023, 23(15), 6668; https://doi.org/10.3390/s23156668 - 25 Jul 2023

Cited by 2 | Viewed by 992

Channel estimation of an orthogonal frequency division multiplexing (OFDM) system based on compressed sensing can effectively reduce the pilot overhead and improve the utilization rate of spectrum resources. The traditional SAMP algorithm with a fixed step size for sparse channel estimation has the [...] Read more.

Channel estimation of an orthogonal frequency division multiplexing (OFDM) system based on compressed sensing can effectively reduce the pilot overhead and improve the utilization rate of spectrum resources. The traditional SAMP algorithm with a fixed step size for sparse channel estimation has the disadvantages of a low estimation efficiency and limited estimation accuracy. An Improved SAMP (ImpSAMP) algorithm is proposed to estimate the channel state information of the OFDM system. In the proposed ImpSAMP algorithm, the received signal is firstly denoised based on the energy-detection method, which can reduce the interferences on channel estimation. Furthermore, the step size is adjusted dynamically according to the l₂ norm of difference between two estimated sparse channel coefficients of adjacent phases to estimate the sparse channel coefficients quickly and accurately. In addition, the double threshold judgment is adopted to enhance the estimation efficiency. The simulation results show that the ImpSAMP algorithm outperforms the traditional SAMP algorithm in estimation efficiency and accuracy. Full article

(This article belongs to the Special Issue Advanced Signal Processing for Next Generation Wireless Communications)

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15 pages, 1161 KiB

Open AccessArticle

Improved Likelihood Probability in MIMO Systems Using One-Bit ADCs

by Tae-Kyoung Kim

Sensors 2023, 23(12), 5542; https://doi.org/10.3390/s23125542 - 13 Jun 2023

Cited by 1 | Viewed by 880

Abstract

This study considers an improved likelihood probability in multi-input multi-output (MIMO) systems using one-bit analog-to-digital converters (ADCs). MIMO systems using one-bit ADCs are known to exhibit from performance degradation because of inaccurate likelihood probabilities. To overcome this degradation, the proposed method leverages the [...] Read more.

This study considers an improved likelihood probability in multi-input multi-output (MIMO) systems using one-bit analog-to-digital converters (ADCs). MIMO systems using one-bit ADCs are known to exhibit from performance degradation because of inaccurate likelihood probabilities. To overcome this degradation, the proposed method leverages the detected symbols to estimate the true likelihood probability by combining the initial likelihood probability. An optimization problem is formulated to minimize the mean-squared error between the true and combined likelihood probabilities, and a solution is derived using the least-squares method. Simulation results show that the proposed method obtains a signal-to-noise gain of approximately

0.3

dB to achieve a frame error rate of

10^{- 1}

compared to conventional methods. This improvement in performance is attributed to the enhanced reliability of the likelihood probability. Full article

(This article belongs to the Special Issue Advanced Signal Processing for Next Generation Wireless Communications)

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21 pages, 958 KiB

Open AccessArticle

Denoising Generalization Performance of Channel Estimation in Multipath Time-Varying OFDM Systems

by Yinying Li, Xin Bian and Mingqi Li

Sensors 2023, 23(6), 3102; https://doi.org/10.3390/s23063102 - 14 Mar 2023

Cited by 2 | Viewed by 1766

Abstract

Although Orthogonal Frequency Division Multiplexing (OFDM) technology is still the key transmission waveform technology in 5G, traditional channel estimation algorithms are no longer sufficient for the high-speed multipath time-varying channels faced by both existing 5G and future 6G. In addition, the existing Deep [...] Read more.

Although Orthogonal Frequency Division Multiplexing (OFDM) technology is still the key transmission waveform technology in 5G, traditional channel estimation algorithms are no longer sufficient for the high-speed multipath time-varying channels faced by both existing 5G and future 6G. In addition, the existing Deep Learning (DL) based OFDM channel estimators are only applicable to Signal-to-Noise Ratios (SNRs) in a small range, and the estimation performance of the existing algorithms is greatly limited when the channel model or the mobile speed at the receiver does not match. To solve this problem, this paper proposes a novel network model NDR-Net that can be used for channel estimation under unknown noise levels. NDR-Net consists of a Noise Level Estimate subnet (NLE), a Denoising Convolutional Neural Network subnet (DnCNN), and a Residual Learning cascade. Firstly, a rough channel estimation matrix value is obtained using the conventional channel estimation algorithm. Then it is modeled as an image and input to the NLE subnet for noise level estimation to obtain the noise interval. Then it is input to the DnCNN subnet together with the initial noisy channel image for noise reduction to obtain the pure noisy image. Finally, the residual learning is added to obtain the noiseless channel image. The simulation results show that NDR-Net can obtain better estimation results than traditional channel estimation, and it can be well adapted when the SNR, channel model, and movement speed do not match, which indicates its superior engineering practicability. Full article

(This article belongs to the Special Issue Advanced Signal Processing for Next Generation Wireless Communications)

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21 pages, 2487 KiB

Open AccessArticle

Direction of Arrival Estimation of Generalized Nested Array via Difference–Sum Co-Array

by Yule Zhang, Guoping Hu, Hao Zhou, Juan Bai, Chenghong Zhan and Shuhan Guo

Sensors 2023, 23(2), 906; https://doi.org/10.3390/s23020906 - 12 Jan 2023

Viewed by 1314

Abstract

To address the weakness that the difference co-array (DCA) only enhances the degrees of freedom (DOFs) to a limited extent, a new configuration called the generalized nested array via difference–sum co-array (GNA-DSCA) is proposed for direction of arrival (DOA) estimation. We consider both [...] Read more.

To address the weakness that the difference co-array (DCA) only enhances the degrees of freedom (DOFs) to a limited extent, a new configuration called the generalized nested array via difference–sum co-array (GNA-DSCA) is proposed for direction of arrival (DOA) estimation. We consider both the temporal and spatial information of the array output to construct the DSCA model, based on which the DCA and sum co-array (SCA) of the GNA are systematically analyzed. The closed-form expression of the DOFs for the GNA-DSCA is derived under the determined dilation factors. The optimal results show that the GNA-DSCA has a more flexible configuration and more DOFs than the GNA-DCA. Moreover, the larger dilation factors yield significantly wider virtual aperture, which indicates that it is more attractive than the reported DSCA-based sparse arrays. Finally, a hole-filling strategy based on atomic norm minimization (ANM) is utilized to overcome the degradation of the estimation performance due to the non-uniform virtual array, thus achieving accurate DOA estimation. The simulation results verify the superiority of the proposed configuration in terms of virtual array properties and estimation performance. Full article

(This article belongs to the Special Issue Advanced Signal Processing for Next Generation Wireless Communications)

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12 pages, 567 KiB

Open AccessCommunication

A Novel Sparse Framework for Angle and Frequency Estimation

by Guilian Zhao, Dongmei Huang, Changxin Cai and Peng Wu

Sensors 2022, 22(22), 8633; https://doi.org/10.3390/s22228633 - 9 Nov 2022

Cited by 1 | Viewed by 1021

Abstract

The topic of joint angle and frequency estimation (JAFE) has aroused extensive interests in the past decades. Current estimation algorithms mainly rely on the Nyquist sampling criterion. In order not to cause ambiguity for parameter estimation, the space–time intervals must be smaller than [...] Read more.

The topic of joint angle and frequency estimation (JAFE) has aroused extensive interests in the past decades. Current estimation algorithms mainly rely on the Nyquist sampling criterion. In order not to cause ambiguity for parameter estimation, the space–time intervals must be smaller than given thresholds, which results in complicated hardware costs and a huge computational burden. This paper aims to reduce the complexity for JAFE, and a novel sparsity-aware framework is proposed. Unlike the current uniform sampling architectures, the incoming narrow-band singles are sampled by a series of space–time coprime samplers. An improved rotational invariance estimator is introduced, which offers closed-form solutions for both angle and frequency estimation. The mathematical treatments indicate that our methodology is inherent in larger spatial/temporal aperture than the uniform sampling architectures; hence, it provides more accurate JAFE compared to alternative approaches relying on uniform sampling. Additionally, it attains nearly the same complexity as the current rotational invariance approach. Numerical results agree with the theoretical advantages of our methodology. Full article

(This article belongs to the Special Issue Advanced Signal Processing for Next Generation Wireless Communications)

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19 pages, 3442 KiB

Open AccessArticle

Kernel Function-Based Ambiguity Function and Its Application on DOA Estimation in Impulsive Noise

by Yuzi Dou and Sen Li

Sensors 2022, 22(18), 6996; https://doi.org/10.3390/s22186996 - 15 Sep 2022

Cited by 3 | Viewed by 1254

Abstract

To solve the problem that the traditional ambiguity function cannot well reflect the time-frequency distribution characteristics of linear frequency modulated (LFM) signals due to the presence of impulsive noise, two robust ambiguity functions: correntropy-based ambiguity function (CRAF) and fractional lower order correntropy-based ambiguity [...] Read more.

To solve the problem that the traditional ambiguity function cannot well reflect the time-frequency distribution characteristics of linear frequency modulated (LFM) signals due to the presence of impulsive noise, two robust ambiguity functions: correntropy-based ambiguity function (CRAF) and fractional lower order correntropy-based ambiguity function (FLOCRAF) are defined based on the feature that correntropy kernel function can effectively suppress impulsive noise. Then these two robust ambiguity functions are used to estimate the direction of arrival (DOA) of narrowband LFM signal under an impulsive noise environment. Instead of the covariance matrix used in the ESPRIT algorithm by the spatial CRAF matrix and FLOCRAF matrix, the CRAF-ESPRIT and FLOCRAF-ESPRIT algorithms are proposed. Computer simulation results show that compared with the algorithms only using ambiguity function and the algorithms only using the correntropy kernel function-based correlation, the proposed algorithms using ambiguity function based on correntropy kernel function have good performance in terms of probability of resolution and estimation accuracy under various circumstances. Especially, the performance of the FLOCRAF-ESPRIT algorithm is better than the CRAF-ESPRIT algorithm in the environment of low generalized signal-to-noise ratio and strong impulsive noise. Full article

(This article belongs to the Special Issue Advanced Signal Processing for Next Generation Wireless Communications)

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14 pages, 381 KiB

Open AccessArticle

Off-Grid DOA Estimation Using Sparse Bayesian Learning for MIMO Radar under Impulsive Noise

by Jitong Ma, Jiacheng Zhang, Zhengyan Yang and Tianshuang Qiu

Sensors 2022, 22(16), 6268; https://doi.org/10.3390/s22166268 - 20 Aug 2022

Cited by 7 | Viewed by 1728

Abstract

Direction of arrival (DOA) estimation is an essential and fundamental part of array signal processing, which has been widely used in radio monitoring, autonomous driving of vehicles, intelligent navigation, etc. However, it remains a challenge to accurately estimate DOA for multiple-input multiple-output (MIMO) [...] Read more.

Direction of arrival (DOA) estimation is an essential and fundamental part of array signal processing, which has been widely used in radio monitoring, autonomous driving of vehicles, intelligent navigation, etc. However, it remains a challenge to accurately estimate DOA for multiple-input multiple-output (MIMO) radar in impulsive noise environments. To address this problem, an off-grid DOA estimation method for monostatic MIMO radar is proposed to deal with non-circular signals under impulsive noise. In the proposed method, firstly, based on the property of non-circular signal and array structure, a virtual array output was built and a real-valued sparse representation for the signal model was constructed. Then, an off-grid sparse Bayesian learning (SBL) framework is proposed and further applied to the virtual array to construct novel off-grid sparse model. Finally, off-grid DOA estimation was realized through the solution of the sparse reconstruction with high accuracy even in impulsive noise. Numerous simulations were performed to compare the algorithm with existing methods. Simulation results verify that the proposed off-grid DOA method enables evident performance improvement in terms of accuracy and robustness compared with other works on impulsive noise. Full article

(This article belongs to the Special Issue Advanced Signal Processing for Next Generation Wireless Communications)

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21 pages, 599 KiB

Open AccessArticle

Low CP Overhead Waveform Design for Multi-Path Channels with Timing Synchronization Error

by Jing Chen, Baobing Wang, Jianzhong Guo, Xin Shan and Dejin Kong

Sensors 2022, 22(15), 5772; https://doi.org/10.3390/s22155772 - 2 Aug 2022

Viewed by 1173

Abstract

In classical orthogonal frequency division multiplexing (OFDM) systems, inserting the cyclic prefix (CP) is necessary before each symbol to overcome the multi-path effect, which, however, occupies numerous time-frequency radio resources, resulting in hampered spectrum efficiency. To address this issue, in this paper, symbol [...] Read more.

In classical orthogonal frequency division multiplexing (OFDM) systems, inserting the cyclic prefix (CP) is necessary before each symbol to overcome the multi-path effect, which, however, occupies numerous time-frequency radio resources, resulting in hampered spectrum efficiency. To address this issue, in this paper, symbol repetition aware OFDM (SR-OFDM) is developed to lower the overhead of CP. In the proposed SR-OFDM, multiple symbols share the same CP with which we examine that the multi-path channels can also be overcome by a simple single-tap equalization without causing any interference. Moreover, after the discrete Fourier transform at the receiver, different symbols are proved to be separated in the time domain, which is beneficial for lowering the demodulation complexity. Furthermore, it is revealed that the above conclusions still hold even under timing synchronization errors, which makes the proposed SR-OFDM favorable in real systems. Extensive simulations validate the efficacy of our proposed SR-OFDM system under the multi-path channels with or without timing synchronization errors. Full article

(This article belongs to the Special Issue Advanced Signal Processing for Next Generation Wireless Communications)

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18 pages, 15812 KiB

Open AccessArticle

A Study on Impact Force Detection Method Based on Piezoelectric Sensing

by Jianli Liu, Chuang Hei, Mingzhang Luo, Dong Yang, Changhe Sun and Ankang Feng

Sensors 2022, 22(14), 5167; https://doi.org/10.3390/s22145167 - 10 Jul 2022

Cited by 4 | Viewed by 2891

Abstract

Impact force refers to a transient phenomenon with a very short-acting time, but a large impulse. Therefore, the detection of impact vibration is critical for the reliability, stability, and overall life of mechanical parts. Accordingly, this paper proposes a method to indirectly characterize [...] Read more.

Impact force refers to a transient phenomenon with a very short-acting time, but a large impulse. Therefore, the detection of impact vibration is critical for the reliability, stability, and overall life of mechanical parts. Accordingly, this paper proposes a method to indirectly characterize the impact force by using an impact stress wave. The LS-DYNA software is utilized to establish the model of a ball impacting the steel plate, and the impact force of the ball and the impact response of the detection point are obtained through explicit dynamic finite element analysis. In addition, on this basis, a correspondence between the impact force and the impact response is established, and finally, an experimental platform for impact force detection is built for experimental testing. The results obtained by the finite element method are in good agreement with the experimental measurement results, and it can be inferred that the detected piezoelectric signal can be used to characterize the impact force. The method proposed herein can guide the impact resistance design and safety assessment of structures in actual engineering applications. Full article

(This article belongs to the Special Issue Advanced Signal Processing for Next Generation Wireless Communications)

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13 pages, 1858 KiB

Open AccessArticle

The DOA Estimation Method for Low-Altitude Targets under the Background of Impulse Noise

by Bin Lin, Guoping Hu, Hao Zhou, Guimei Zheng and Yuwei Song

Sensors 2022, 22(13), 4853; https://doi.org/10.3390/s22134853 - 27 Jun 2022

Cited by 4 | Viewed by 1661

Abstract

Due to the discontinuity of ocean waves and mountains, there are often multipath propagation effects and obvious pulse characteristics in low-altitude detection. If the conventional direction of arrival (DOA) estimation method is directly used for direction finding, it will lead to a large [...] Read more.

Due to the discontinuity of ocean waves and mountains, there are often multipath propagation effects and obvious pulse characteristics in low-altitude detection. If the conventional direction of arrival (DOA) estimation method is directly used for direction finding, it will lead to a large error. In view of serious misalignment in the DOA estimation of multipath signals under the background of impulse noise, a DOA estimation method based on spatial difference and a modified projection subspace algorithm is proposed in this paper. Firstly, the covariance matrix of the received data vector is used for spatial difference to eliminate the multipath effects of low-altitude targets. Secondly, the modified projection matrix is constructed using the signal source estimated with the least squares criterion and then used for modifying the covariance matrix, thus eliminating the cross-covariance matrices that affect the estimation accuracy. Finally, the modified covariance matrix is used for the DOA estimation of targets. Simulations show that the proposed algorithm achieves a higher accuracy in the DOA estimation of low-altitude targets than conventional algorithms under two common impulse noise models, without requiring prior knowledge of impulse noise. Full article

(This article belongs to the Special Issue Advanced Signal Processing for Next Generation Wireless Communications)

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14 pages, 4555 KiB

Open AccessArticle

Joint Estimation Method of DOD and DOA of Bistatic Coprime Array MIMO Radar for Coherent Targets Based on Low-Rank Matrix Reconstruction

by Zhiyuan You, Guoping Hu, Hao Zhou and Guimei Zheng

Sensors 2022, 22(12), 4625; https://doi.org/10.3390/s22124625 - 19 Jun 2022

Cited by 3 | Viewed by 1571

Abstract

Based on low-rank matrix reconstruction theory, this paper proposes a joint DOD and DOA estimation method for coherent targets with bistatic coprime array MIMO radar. Unlike the conventional vectorization, the proposed method processed the coprime array with virtual sensor interpolation, which obtained a [...] Read more.

Based on low-rank matrix reconstruction theory, this paper proposes a joint DOD and DOA estimation method for coherent targets with bistatic coprime array MIMO radar. Unlike the conventional vectorization, the proposed method processed the coprime array with virtual sensor interpolation, which obtained a uniform linear array to generate the covariance matrix. Then, we reconstructed the Toeplitz matrix and established a matrix optimization recovery model according to the kernel norm minimization theory. Finally, the reduced dimension multiple signal classification algorithm was applied to estimate the angle of the coherent targets, with which the automatic pairing of DOD and DOA could be realized. With the same number of physical sensors, the proposed method expanded the array aperture effectively, so that the degree of freedom and angular resolution could be improved significantly for coherent signals. However, the effectiveness of the method was largely limited by the signal-to-noise ratio. The superiority and effectiveness of the method were proved using simulation experiments. Full article

(This article belongs to the Special Issue Advanced Signal Processing for Next Generation Wireless Communications)

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16 pages, 4276 KiB

Open AccessArticle

Experimental Study on Shear Wave Transmission in Fractured Media

by Ming Cai, Hongliang Wu, Yi Xin, Peng Liu, Chengguang Zhang, Jun Tang, Minjie Lin and Lihong Tan

Sensors 2022, 22(11), 4047; https://doi.org/10.3390/s22114047 - 26 May 2022

Cited by 5 | Viewed by 1424

Abstract

Unconventional oil and gas reservoirs have broad exploration and development prospects. Fracture parameters and effectiveness evaluation are two of the key tasks for the evaluation of these types of reservoirs. Array acoustic logging can be used for fracture evaluation to compensate for the [...] Read more.

Unconventional oil and gas reservoirs have broad exploration and development prospects. Fracture parameters and effectiveness evaluation are two of the key tasks for the evaluation of these types of reservoirs. Array acoustic logging can be used for fracture evaluation to compensate for the deficiencies of the image logging fracture evaluation method. Therefore, to develop acoustic logging evaluation methods as well as nondestructive testing methods for fractures, experiments were conducted to study the shear wave transmission in fractured media. Experiment data demonstrate a good correlation between the shear wave attenuation coefficient and fracture width, and the shear wave attenuation coefficients rise logarithmically with the increase in the fracture width for all models with different porosities and distinct dip angles of fractures. The shear wave attenuation coefficient changes relatively faster with the fracture width when the fracture width is within 250 μm. In addition, the shear wave attenuation is affected by the core porosity and fracture dip angle. When the fracture width is constant, the shear wave attenuation caused by the 0° fracture is relatively larger and is obviously greater than that of the fractures at other angles, which is consistent with the existing experimental results. The results of this study can be used to guide further research on amplitude compensation methods for sonic signal transmission in fractured media and fracture evaluation methods. Full article

(This article belongs to the Special Issue Advanced Signal Processing for Next Generation Wireless Communications)

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