{"title":"Correction to “Energy Efficient Signal Detection Using SPRT and Ordered Transmissions in Wireless Sensor Networks”","authors":"Shailee Yagnik;Ramanarayanan Viswanathan;Lei Cao","doi":"10.1109/OJSP.2024.3519916","DOIUrl":"https://doi.org/10.1109/OJSP.2024.3519916","url":null,"abstract":"In [1, p. 1124], a footnote is needed on (13) as shown below: begin{equation*}qquadqquadquad{{alpha }^# } < left( {1 - {{c}_1}} right)alpha + left( {1 - left( {1 - {{c}_1}} right)alpha } right)alphaqquadqquadquad hbox{(13)$^{1}$} end{equation*}","PeriodicalId":73300,"journal":{"name":"IEEE open journal of signal processing","volume":"6 ","pages":"16-16"},"PeriodicalIF":2.9,"publicationDate":"2025-01-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10845022","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142993599","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-12-13DOI: 10.1109/OJSP.2024.3498352
{"title":"List of Reviewers","authors":"","doi":"10.1109/OJSP.2024.3498352","DOIUrl":"https://doi.org/10.1109/OJSP.2024.3498352","url":null,"abstract":"","PeriodicalId":73300,"journal":{"name":"IEEE open journal of signal processing","volume":"5 ","pages":"1153-1155"},"PeriodicalIF":2.9,"publicationDate":"2024-12-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10799210","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142821148","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Noise mitigation proves to be a challenging task for active noise control in the existence of nonlinearities. In such environments, functional link neural network (FLN) and adaptive exponential FLN techniques improve the performance of distributed active noise control systems. Nonlinear spline approaches are well known for their low computational complexity and ability to effectively alleviate noise in nonlinear systems. This paper proposes a new cost function for distributed active noise control (DANC) system which is based on the Charbonnier quasi hyperbolic momentum spline (CQHMS) involving incremental approach. This incremental based CQHMS DANC method employs Charbonnier loss and quasi hyperbolic momentum approach which minimizes gradient variance and local crossover points in order to enhance the convergence and steady-state performance. The technique being proposed demonstrates enhanced performance and achieves accelerated convergence when compared to existing techniques in a range of nonlinear DANC scenarios in lieu of varied nonlinear primary path and nonlinear secondary path conditions.
{"title":"Charbonnier Quasi Hyperbolic Momentum Spline Based Incremental Strategy for Nonlinear Distributed Active Noise Control","authors":"Rajapantula Kranthi;Vasundhara;Asutosh Kar;Mads Græsbøll Christensen","doi":"10.1109/OJSP.2024.3501774","DOIUrl":"https://doi.org/10.1109/OJSP.2024.3501774","url":null,"abstract":"Noise mitigation proves to be a challenging task for active noise control in the existence of nonlinearities. In such environments, functional link neural network (FLN) and adaptive exponential FLN techniques improve the performance of distributed active noise control systems. Nonlinear spline approaches are well known for their low computational complexity and ability to effectively alleviate noise in nonlinear systems. This paper proposes a new cost function for distributed active noise control (DANC) system which is based on the Charbonnier quasi hyperbolic momentum spline (CQHMS) involving incremental approach. This incremental based CQHMS DANC method employs Charbonnier loss and quasi hyperbolic momentum approach which minimizes gradient variance and local crossover points in order to enhance the convergence and steady-state performance. The technique being proposed demonstrates enhanced performance and achieves accelerated convergence when compared to existing techniques in a range of nonlinear DANC scenarios in lieu of varied nonlinear primary path and nonlinear secondary path conditions.","PeriodicalId":73300,"journal":{"name":"IEEE open journal of signal processing","volume":"6 ","pages":"1-15"},"PeriodicalIF":2.9,"publicationDate":"2024-11-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10759299","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142937903","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-11-11DOI: 10.1109/OJSP.2024.3496819
Chang Sun;Bo Qin;Hong Yang
Visual Speech Recognition (VSR) tasks are generally recognized to have a lower theoretical performance ceiling than Automatic Speech Recognition (ASR), owing to the inherent limitations of conveying semantic information visually. To mitigate this challenge, this paper introduces an advanced knowledge distillation approach using a Joint-Embedding Predictive Architecture (JEPA), JEP-KD, designed to utilize audio features more effectively during model training. Central to JEP-KD is including a generative network within the embedding layer in the knowledge distillation structure, which enhances the video encoder's capacity for semantic feature extraction and brings it closer to the audio features from a pre-trained ASR model's encoder. This approach aims to reduce the performance gap between VSR and ASR progressively. Moreover, a comprehensive multimodal, multistage training regimen for the JEP-KD framework is established, bolstering the robustness and efficacy of the training process. Experiment results demonstrate that JEP-KD significantly improves the performance of VSR models and demonstrates versatility across different VSR platforms, indicating its potential for broader application within other multimodal tasks.
{"title":"JEP-KD: Joint-Embedding Predictive Architecture Based Knowledge Distillation for Visual Speech Recognition","authors":"Chang Sun;Bo Qin;Hong Yang","doi":"10.1109/OJSP.2024.3496819","DOIUrl":"https://doi.org/10.1109/OJSP.2024.3496819","url":null,"abstract":"Visual Speech Recognition (VSR) tasks are generally recognized to have a lower theoretical performance ceiling than Automatic Speech Recognition (ASR), owing to the inherent limitations of conveying semantic information visually. To mitigate this challenge, this paper introduces an advanced knowledge distillation approach using a Joint-Embedding Predictive Architecture (JEPA), JEP-KD, designed to utilize audio features more effectively during model training. Central to JEP-KD is including a generative network within the embedding layer in the knowledge distillation structure, which enhances the video encoder's capacity for semantic feature extraction and brings it closer to the audio features from a pre-trained ASR model's encoder. This approach aims to reduce the performance gap between VSR and ASR progressively. Moreover, a comprehensive multimodal, multistage training regimen for the JEP-KD framework is established, bolstering the robustness and efficacy of the training process. Experiment results demonstrate that JEP-KD significantly improves the performance of VSR models and demonstrates versatility across different VSR platforms, indicating its potential for broader application within other multimodal tasks.","PeriodicalId":73300,"journal":{"name":"IEEE open journal of signal processing","volume":"5 ","pages":"1147-1152"},"PeriodicalIF":2.9,"publicationDate":"2024-11-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10750407","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142810545","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-11-11DOI: 10.1109/OJSP.2024.3496815
Yongsung Park;Peter Gerstoft;Christoph F. Mecklenbräuker
This paper proposes gridless sparse direction-of-arrival (DOA) refinement using gradient-based optimization. The objective function minimizes the fit between the sample covariance matrix (SCM) and a reconstructed covariance matrix. The latter is constrained to contain only a few atoms, but otherwise maximally matches the SCM. This reconstruction enables analytic derivatives with respect to DOA using Wirtinger gradients. The sensitivity of the solution to local minima is addressed by initializing near the true DOAs, where a user-input-free gridded sparse Bayesian learning is employed. Numerical results validate the effectiveness of the DOA refinement using analytic gradients, demonstrating its ability to reach the Cramér-Rao bound and achieve higher resolution compared to conventional gridless DOA estimation methods. The approach is validated by considering different numbers of DOAs, grid sizes, DOAs on/off the grid, fewer (even a single) snapshots, coherent arrivals, closely separated DOAs, and many DOAs.
本文提出利用基于梯度的优化方法进行无网格稀疏到达方向（DOA）细化。目标函数最小化样本协方差矩阵（SCM）与重建协方差矩阵之间的拟合。重建的协方差矩阵受限于只包含几个原子，但在其他方面最大限度地与 SCM 匹配。通过这种重建方法，可以利用 Wirtinger 梯度对 DOA 进行分析求导。通过在真实 DOA 附近进行初始化，解决了求解对局部最小值的敏感性问题。数值结果验证了使用解析梯度进行 DOA 精化的有效性，表明与传统的无网格 DOA 估算方法相比，该方法能够达到 Cramér-Rao 约束并实现更高的分辨率。通过考虑不同的 DOA 数量、网格大小、网格内/外的 DOA、较少（甚至单一）的快照、相干到达、紧密分离的 DOA 以及许多 DOA，验证了该方法的有效性。
{"title":"Atom-Constrained Gridless DOA Refinement With Wirtinger Gradients","authors":"Yongsung Park;Peter Gerstoft;Christoph F. Mecklenbräuker","doi":"10.1109/OJSP.2024.3496815","DOIUrl":"https://doi.org/10.1109/OJSP.2024.3496815","url":null,"abstract":"This paper proposes gridless sparse direction-of-arrival (DOA) refinement using gradient-based optimization. The objective function minimizes the fit between the sample covariance matrix (SCM) and a reconstructed covariance matrix. The latter is constrained to contain only a few atoms, but otherwise maximally matches the SCM. This reconstruction enables analytic derivatives with respect to DOA using Wirtinger gradients. The sensitivity of the solution to local minima is addressed by initializing near the true DOAs, where a user-input-free gridded sparse Bayesian learning is employed. Numerical results validate the effectiveness of the DOA refinement using analytic gradients, demonstrating its ability to reach the Cramér-Rao bound and achieve higher resolution compared to conventional gridless DOA estimation methods. The approach is validated by considering different numbers of DOAs, grid sizes, DOAs on/off the grid, fewer (even a single) snapshots, coherent arrivals, closely separated DOAs, and many DOAs.","PeriodicalId":73300,"journal":{"name":"IEEE open journal of signal processing","volume":"5 ","pages":"1134-1146"},"PeriodicalIF":2.9,"publicationDate":"2024-11-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10750433","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142821229","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-11-11DOI: 10.1109/OJSP.2024.3495553
Jinho Choi
In order to extract governing equations from time-series data, various approaches are proposed. Among those, sparse identification of nonlinear dynamics (SINDy) stands out as a successful method capable of modeling governing equations with a minimal number of terms, utilizing the principles of compressive sensing. This feature, which relies on a small number of terms, is crucial for interpretability. The effectiveness of SINDy hinges on the choice of candidate functions within its dictionary to extract governing equations of dynamical systems. A larger dictionary allows for more terms, enhancing the quality of approximations. However, the computational complexity scales with dictionary size, rendering SINDy less suitable for high-dimensional datasets, even though it has been successfully applied to low-dimensional datasets. To address this challenge, we introduce iterative SINDy in this paper, where the dictionary undergoes expansion and compression through iterations. We also conduct an analysis of the convergence properties of iterative SINDy. Simulation results validate that iterative SINDy can achieve nearly identical performance to SINDy, while significantly reducing computational complexity. Notably, iterative SINDy demonstrates effectiveness with high-dimensional time-series data without incurring the prohibitively high computational cost associated with SINDy.
{"title":"Iterative Sparse Identification of Nonlinear Dynamics","authors":"Jinho Choi","doi":"10.1109/OJSP.2024.3495553","DOIUrl":"https://doi.org/10.1109/OJSP.2024.3495553","url":null,"abstract":"In order to extract governing equations from time-series data, various approaches are proposed. Among those, sparse identification of nonlinear dynamics (SINDy) stands out as a successful method capable of modeling governing equations with a minimal number of terms, utilizing the principles of compressive sensing. This feature, which relies on a small number of terms, is crucial for interpretability. The effectiveness of SINDy hinges on the choice of candidate functions within its dictionary to extract governing equations of dynamical systems. A larger dictionary allows for more terms, enhancing the quality of approximations. However, the computational complexity scales with dictionary size, rendering SINDy less suitable for high-dimensional datasets, even though it has been successfully applied to low-dimensional datasets. To address this challenge, we introduce iterative SINDy in this paper, where the dictionary undergoes expansion and compression through iterations. We also conduct an analysis of the convergence properties of iterative SINDy. Simulation results validate that iterative SINDy can achieve nearly identical performance to SINDy, while significantly reducing computational complexity. Notably, iterative SINDy demonstrates effectiveness with high-dimensional time-series data without incurring the prohibitively high computational cost associated with SINDy.","PeriodicalId":73300,"journal":{"name":"IEEE open journal of signal processing","volume":"5 ","pages":"1107-1118"},"PeriodicalIF":2.9,"publicationDate":"2024-11-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10750024","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142691822","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-10-30DOI: 10.1109/OJSP.2024.3488530
Shailee Yagnik;Ramanarayanan Viswanathan;Lei Cao
In a distributed detection system with multiple sensors, the ordered transmission scheme (OTS) proposed by Blum and Sadler requires a fewer number of transmissions in comparison with a fixed sample size test with the same probability of error performance. In this work, we propose an ordered transmission scheme using a truncated sequential probability ratio test (SPRT), termed as OSPRT. With a suitable choice of two design parameters, the probability of error of the OSPRT can be upper bounded by no more than a certain percentage above the probability of error of OTS, yet achieving significant savings in both the average number of samples needed to arrive at a decision, and the average energy in signal transmission. The superiority of ordered transmissions over unordered transmissions is quantified in terms of Kullback-Leibler information. Simulation analysis for the detection of a constant signal of moderate strength in Gaussian noise shows that the probability of error of OSPRT, which is substantially below the theoretical upper bound, is only negligibly larger than the OTS error. Analysis also shows that OSPRT is more energy efficient than the original OTS.
{"title":"Energy Efficient Signal Detection Using SPRT and Ordered Transmissions in Wireless Sensor Networks","authors":"Shailee Yagnik;Ramanarayanan Viswanathan;Lei Cao","doi":"10.1109/OJSP.2024.3488530","DOIUrl":"https://doi.org/10.1109/OJSP.2024.3488530","url":null,"abstract":"In a distributed detection system with multiple sensors, the ordered transmission scheme (OTS) proposed by Blum and Sadler requires a fewer number of transmissions in comparison with a fixed sample size test with the same probability of error performance. In this work, we propose an ordered transmission scheme using a truncated sequential probability ratio test (SPRT), termed as OSPRT. With a suitable choice of two design parameters, the probability of error of the OSPRT can be upper bounded by no more than a certain percentage above the probability of error of OTS, yet achieving significant savings in both the average number of samples needed to arrive at a decision, and the average energy in signal transmission. The superiority of ordered transmissions over unordered transmissions is quantified in terms of Kullback-Leibler information. Simulation analysis for the detection of a constant signal of moderate strength in Gaussian noise shows that the probability of error of OSPRT, which is substantially below the theoretical upper bound, is only negligibly larger than the OTS error. Analysis also shows that OSPRT is more energy efficient than the original OTS.","PeriodicalId":73300,"journal":{"name":"IEEE open journal of signal processing","volume":"5 ","pages":"1119-1133"},"PeriodicalIF":2.9,"publicationDate":"2024-10-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10738433","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142713893","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-10-02DOI: 10.1109/OJSP.2024.3473610
Petre Stoica;Prabhu Babu;Piyush Varshney
The robust estimation of the covariance matrix is a frequent task in practical applications in which, more often than not, some data samples are outliers. There are several methods that can be used to robustly estimate a covariance matrix from corrupted data, a representative example of which is the m inimum c ovariance d eterminant (MCD) method. In this paper we present a maximum conditional likelihood interpretation of MCD that provides a new motivation of as well as further insights into this method. To perform at its best MCD requires information on the number of outliers in the data, which usually is not available. We propose two new methods for covariance matrix estimation from data with outliers that do not suffer from this problem: TEST (multiple-hypothesis test ing method) which uses the FDR (false discovery rate) to test a set of model hypotheses and hence estimate the number of outliers and their locations, and LIKE (penalized like lihood method) that solves the outlier estimation problem using a GIC (generalized information criterion) to penalize the complexity of a high-dimensional data model. We show by means of numerical simulations that the performances of TEST and LIKE are relatively similar to one another as well as to the performance of the oracle MCD (which uses the true number of outliers) and significantly better than the performance of MCD that uses an upper bound on the outlier number.
{"title":"Robust Estimation of the Covariance Matrix From Data With Outliers","authors":"Petre Stoica;Prabhu Babu;Piyush Varshney","doi":"10.1109/OJSP.2024.3473610","DOIUrl":"https://doi.org/10.1109/OJSP.2024.3473610","url":null,"abstract":"The robust estimation of the covariance matrix is a frequent task in practical applications in which, more often than not, some data samples are outliers. There are several methods that can be used to robustly estimate a covariance matrix from corrupted data, a representative example of which is the \u0000<bold>m</b>\u0000inimum \u0000<bold>c</b>\u0000ovariance \u0000<bold>d</b>\u0000eterminant (MCD) method. In this paper we present a maximum conditional likelihood interpretation of MCD that provides a new motivation of as well as further insights into this method. To perform at its best MCD requires information on the number of outliers in the data, which usually is not available. We propose two new methods for covariance matrix estimation from data with outliers that do not suffer from this problem: TEST (multiple-hypothesis \u0000<bold>test</b>\u0000ing method) which uses the FDR (false discovery rate) to test a set of model hypotheses and hence estimate the number of outliers and their locations, and LIKE (penalized \u0000<bold>like</b>\u0000lihood method) that solves the outlier estimation problem using a GIC (generalized information criterion) to penalize the complexity of a high-dimensional data model. We show by means of numerical simulations that the performances of TEST and LIKE are relatively similar to one another as well as to the performance of the oracle MCD (which uses the true number of outliers) and significantly better than the performance of MCD that uses an upper bound on the outlier number.","PeriodicalId":73300,"journal":{"name":"IEEE open journal of signal processing","volume":"5 ","pages":"1061-1072"},"PeriodicalIF":2.9,"publicationDate":"2024-10-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10704043","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142565595","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
In this paper, we consider a sensor placement problem where sensors can move within a network over time. Sensor placement problem aims to select $K$ sensor positions from $N$ candidates where $K < N$ . Most existing methods assume that sensor positions are static, i.e., they do not move, however, many mobile sensors like drones, robots, and vehicles can change their positions over time. Moreover, underlying measurement conditions could also be changed, which are difficult to cover with statically placed sensors. We tackle the problem by allowing the sensors to change their positions in their neighbors on the network. We dynamically determine the sensor positions based on graph signal sampling theory such that the non-observed signals on the network can be best recovered from the observations. For signal recovery, the dictionary is learned from a pool of observed signals. It is also used for the sensor position selection. In experiments, we validate the effectiveness of the proposed method via the mean squared error of the reconstructed signals. The proposed dynamic sensor placement outperforms the existing static ones for both synthetic and real data.
{"title":"Dynamic Sensor Placement Based on Sampling Theory for Graph Signals","authors":"Saki Nomura;Junya Hara;Hiroshi Higashi;Yuichi Tanaka","doi":"10.1109/OJSP.2024.3466133","DOIUrl":"https://doi.org/10.1109/OJSP.2024.3466133","url":null,"abstract":"In this paper, we consider a sensor placement problem where sensors can move within a network over time. Sensor placement problem aims to select \u0000<inline-formula><tex-math>$K$</tex-math></inline-formula>\u0000 sensor positions from \u0000<inline-formula><tex-math>$N$</tex-math></inline-formula>\u0000 candidates where \u0000<inline-formula><tex-math>$K < N$</tex-math></inline-formula>\u0000. Most existing methods assume that sensor positions are static, i.e., they do not move, however, many mobile sensors like drones, robots, and vehicles can change their positions over time. Moreover, underlying measurement conditions could also be changed, which are difficult to cover with statically placed sensors. We tackle the problem by allowing the sensors to change their positions in their neighbors on the network. We dynamically determine the sensor positions based on graph signal sampling theory such that the non-observed signals on the network can be best recovered from the observations. For signal recovery, the dictionary is learned from a pool of observed signals. It is also used for the sensor position selection. In experiments, we validate the effectiveness of the proposed method via the mean squared error of the reconstructed signals. The proposed dynamic sensor placement outperforms the existing static ones for both synthetic and real data.","PeriodicalId":73300,"journal":{"name":"IEEE open journal of signal processing","volume":"5 ","pages":"1042-1051"},"PeriodicalIF":2.9,"publicationDate":"2024-09-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10689379","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142518001","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-09-02DOI: 10.1109/OJSP.2024.3453176
Marcele O. K. Mendonça;Paulo S. R. Diniz;Javier Maroto Morales;Pascal Frossard
Orthogonal frequency-division multiplexing (OFDM) is widely used to mitigate inter-symbol interference (ISI) from multipath fading. However, the open nature of wireless OFDM systems makes them vulnerable to jamming attacks. In this context, pilot jamming is critical as it focuses on corrupting the symbols used for channel estimation and equalization, degrading the system performance. Although neural networks (NNs) can improve channel estimation and mitigate pilot jamming penalty, they are also themselves susceptible to malicious perturbations known as adversarial examples. If the jamming attack is crafted in order to fool the NN, it represents an adversarial example that impairs the proper behavior of OFDM systems. In this work, we explore two machine learning (ML)-based jamming strategies that are especially intended to degrade the performance of ML-based channel estimators, in addition to a traditional Additive White Gaussian Noise (AWGN) jamming attack. These ML-based attacks create noise patterns designed to reduce the precision of the channel estimation process, thereby compromising the reliability and robustness of the communication system. We highlight the vulnerabilities of wireless communication systems to ML-based pilot jamming attacks that corrupts symbols used for channel estimation, leading to system performance degradation. To mitigate these threats, this paper proposes an adversarial training defense mechanism desined to counter jamming attacks. The effectiveness of this defense is validated through simulation results, demonstrating improved channel estimation performance in the presence of jamming attacks. The proposed defense methods aim to enhance the resilience of OFDM systems against pilot jamming attacks, ensuring more robust communication in wireless environments.
正交频分复用（OFDM）被广泛用于缓解多径衰落造成的符号间干扰（ISI）。然而，无线 OFDM 系统的开放性使其容易受到干扰攻击。在这种情况下，先导干扰至关重要，因为它主要会破坏用于信道估计和均衡的符号，从而降低系统性能。虽然神经网络（NN）可以改善信道估计并减轻先导干扰的惩罚，但它们本身也容易受到被称为对抗范例的恶意扰动的影响。如果干扰攻击是为了愚弄神经网络而精心设计的，那么它就代表了一种损害 OFDM 系统正常行为的对抗范例。在这项工作中，除了传统的加性白高斯噪声（AWGN）干扰攻击外，我们还探索了两种基于机器学习（ML）的干扰策略，其目的是降低基于 ML 的信道估计器的性能。这些基于 ML 的攻击会产生噪音模式，旨在降低信道估计过程的精度，从而损害通信系统的可靠性和鲁棒性。我们强调了无线通信系统在基于 ML 的先导干扰攻击面前的脆弱性，这种攻击会破坏用于信道估计的符号，从而导致系统性能下降。为了减轻这些威胁，本文提出了一种对抗性训练防御机制，旨在对抗干扰攻击。这种防御机制的有效性通过仿真结果得到了验证，证明了在存在干扰攻击的情况下信道估计性能的提高。所提出的防御方法旨在增强 OFDM 系统抵御先导干扰攻击的能力，确保在无线环境中实现更稳健的通信。
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