Low Signal-to-noise Ratio Scenarios Research Articles

Acoustic leak localization for water distribution network through time-delay-based deep learning approach

Water leakage within water distribution networks (WDNs) presents significant challenges, encompassing infrastructure damage, economic losses, and public health risks. Traditional methods for leak localization based on acoustic signals encounter inherent limitations due to environmental noise and signal distortions. In response to this crucial issue, this study introduces an innovative approach that utilizes deep learning-based techniques to estimate time delay for leak localization. The research findings reveal that while the Res1D-CNN model demonstrates inferior performance compared to the GCC-SCOT and BCC under high signal-to-noise ratio (SNR) conditions, it exhibits robust capabilities and higher accuracy in low SNR scenarios. The proposed method's efficacy was empirically validated through field measurements. This advancement in acoustic leak localization holds the potential to significantly improve fault diagnosis and maintenance systems, thereby enabling efficient management of WDNs.

Enhancing radio signal classification under low SNR conditions using deep residual networks with channel attention mechanisms

This paper delves into the advancement of deep residual networks (ResNets) integrated with channel attention mechanisms for the classification of radio signals under conditions of low Signal-to-Noise Ratio (SNR). Utilizing an expansive dataset of radio signals, this paper introduces a novel architecture, MyResNet1, that combines residual learning with channel-wise attention, allowing the model to concentrate on essential features for precise classification. My, investigations exhibit notable improvements in classification accuracy, especially in challenging low SNR scenarios, highlighting the potential of attention-augmented deep residual networks in radio signal processing. Furthermore, this studyexplores various optimization strategies, including data augmentation and regularization techniques, to enhance the models performance and robustness. My findings contribute significantly to cognitive radio technologies and illuminate the potential of deep learning in sophisticated signal classification tasks, aligned with recent explorations in automatic modulation recognition (AMR) through deep learning and autoencoder-based methodologies for enhancing I/Q channel interactions.

Simcryocluster: a semantic similarity clustering method of cryo-EM images by adopting contrastive learning

BackgroundCryo-electron microscopy (Cryo-EM) plays an increasingly important role in the determination of the three-dimensional (3D) structure of macromolecules. In order to achieve 3D reconstruction results close to atomic resolution, 2D single-particle image classification is not only conducive to single-particle selection, but also a key step that affects 3D reconstruction. The main task is to cluster and align 2D single-grain images into non-heterogeneous groups to obtain sharper single-grain images by averaging calculations. The main difficulties are that the cryo-EM single-particle image has a low signal-to-noise ratio (SNR), cannot manually label the data, and the projection direction is random and the distribution is unknown. Therefore, in the low SNR scenario, how to obtain the characteristic information of the effective particles, improve the clustering accuracy, and thus improve the reconstruction accuracy, is a key problem in the 2D image analysis of single particles of cryo-EM.ResultsAiming at the above problems, we propose a learnable deep clustering method and a fast alignment weighted averaging method based on frequency domain space to effectively improve the class averaging results and improve the reconstruction accuracy. In particular, it is very prominent in the feature extraction and dimensionality reduction module. Compared with the classification method based on Bayesian and great likelihood, a large amount of single particle data is required to estimate the relative angle orientation of macromolecular single particles in the 3D structure, and we propose that the clustering method shows good results.ConclusionsSimcryoCluster can use the contrastive learning method to perform well in the unlabeled high-noise cryo-EM single particle image classification task, making it an important tool for cryo-EM protein structure determination

Joint mode selection and resource allocation to flexibly guarantee quality of service for downlink MISO-NOMA systems

Non-orthogonal multiple access (NOMA) with successive interference cancellation (SIC) detection, knownas SIC NOMA, enjoys higher spectral efficiency (SE), but suffers increased processing complexity and additional error propagation compared to Non-SIC NOMA,in whichthe receivers directly detect their own signalsfrom thesuperposed signalwithout SIC detection. We propose a joint Mode Selection and Power Allocation (MSPA) scheme that can flexibly realize different kinds of tradeoffs while guaranteeing minimum rate requirements (MRRs) for two-user MISO-NOMA downlink. Specifically, we first obtain rate region boundaries that guarantee two users’ MRRs for two candidatemodes: maximum-ratio transmission (MRT)/SIC NOMA and minimum mean square error beamforming (MMSE-BF)/Non-SIC NOMA. We then construct an expression of power allocation factors (transmit power factors) that can satisfy the two users’ MRRs for MRT/SIC NOMA (MMSE-BF/Non-SIC NOMA) by introducing a parameter λ. Finally, we achieve outer boundary of the union ofthe two modes' rate regionsby mode selection and derive expected users' rates on it. Numerical results demonstrate that the proposed MSPA can improve SE and flexibly achieve different kinds of tradeoffs by adjusting λ, while guaranteeing the two users’ MRRs simultaneously for high signal to noise ratio (SNR) scenario and alternately guaranteeing the two users’ MRRs for low SNR scenario.

Performance Improvement of CDMA Wireless Sensor Networks in Low SNR Channels Based on Raptor Codes

Low signal to noise ratio (SNR) channels require careful design of communication systems in order to operate reliably in such extreme channels. Special measures and techniques must be considered when designing communication systems for such environments to counteract the effect of high noise power. Channel coding is a powerful tool to aid against adverse channel conditions including low SNR. Coupling channel coding with a spreading technique can further strengthen the performance of communication systems. Hence, we studied the performance of a system that combines Raptor codes with direct sequence code division multiple access (DS-CDMA) in low SNR scenarios. The studied design can operate in a wide range of low SNR values. Simulation results show that the system can offer satisfactory BER performance at different code rates. Also, combining Raptor codes with DS-CDMA showed an improvement of approximately 6 dB when compared with other schemes using DS-CDMA in low SNR conditions.

Toward Length-Versatile and Noise-Robust Radio Frequency Fingerprint Identification

Radio frequency fingerprint identification (RFFI) can classify wireless devices by analyzing the signal distortions caused by intrinsic hardware impairments. Recently, state-of-the-art neural networks have been adopted for RFFI. However, many neural networks, e.g., multilayer perceptron (MLP) and convolutional neural network (CNN), require fixed-size input data. In addition, many IoT devices work in low signal-to-noise ratio (SNR) scenarios but the RFFI performance in such scenarios is often unsatisfactory. In this paper, we analyze the reason why MLP- and CNN-based RFFI systems are constrained by the input size. To overcome this, we propose four neural networks that can process signals of variable lengths, namely flatten-free CNN, long short-term memory (LSTM) network, gated recurrent unit (GRU) network, and transformer. We adopt data augmentation during training which can significantly improve the model’s robustness to noise. We compare two augmentation schemes, namely offline and online augmentation. The results show the online one performs better. During the inference, a multi-packet inference approach is further leveraged to improve the classification accuracy in low SNR scenarios. We take LoRa as a case study and evaluate the system by classifying 10 commercial-off-the-shelf LoRa devices in various SNR conditions. The online augmentation can boost the low-SNR classification accuracy by up to 50% and the multi-packet inference approach can further increase the accuracy by over 20%.

Multi-Target Parameter Estimation of the FMCW-MIMO Radar Based on the Pseudo-Noise Resampling Method.

Subspace methods are widely used in FMCW-MIMO radars for target parameter estimations. However, the performances of the existing algorithms degrade rapidly in non-ideal situations. For example, a small number of snapshots may result in the distortion of the covariance matrix estimation and a low signal-to-noise ratio (SNR) can lead to subspace leakage problems, which affects the parameter estimation accuracy. In this paper, a joint DOA-range estimation algorithm is proposed to solve the above issues. Firstly, the improved unitary root-MUSIC algorithm is applied to reduce the influence of non-ideal terms in building the covariance matrix. Subsequently, the least squares method is employed to process the data and obtain paired range estimation. However, in a small number of snapshots and low SNR scenarios, even if the impact of non-ideal terms is reduced, there will still be cases where the estimators sometimes deviate from the true target. The estimators that deviate greatly from targets are regarded as outliers. Therefore, threshold detection is applied to determine whether outliers exist. After that, a pseudo-noise resampling (PR) technology is proposed to form a new data observation matrix, which further alleviates the error of the estimators. The proposed method overcomes performance degradation in a small number of snapshots or low SNRs simultaneously. Theoretical analyses and simulation results demonstrate the effectiveness and superiority.

DCCTN: Deep Complex Convolution Transformer Network for Far-field and Low SNR Speech Enhancement

Single-channel speech enhancement has made great progress with the development of deep learning. Recently, some researchers predict the real and imaginary parts of the output respectively by deep complex convolutional networks and achieve state-of-art performance. Based on this, in this paper, we design a new network structure, called Deep Complex Convolution Transformer Network (DCCTN), which is dedicated to solving the problem of single-channel speech enhancement under the conditions of far-field and extremely low signal-to-noise ratio (SNR). First, based on the Deep Complex Convolution Recurrent Network (DCCRN), a two-stage transformer masking module is introduced to replace the recurrent network structure and better focus on the long-term correlation of speech. Secondly, the deep complex transformer structure and gaussian weight matrix are introduced to make the model more suitable for far-field and very low SNR scenarios. In the experiment, we mixed real far-field clean speech and noise to make training and test data sets. The experimental results show that in the target scene, DCCTN proposed has significantly improved speech-enhancement performance, compared to most of the latest methods.

Subject-Dependent Artifact Removal for Enhancing Motor Imagery Classifier Performance under Poor Skills.

The Electroencephalography (EEG)-based motor imagery (MI) paradigm is one of the most studied technologies for Brain-Computer Interface (BCI) development. Still, the low Signal-to-Noise Ratio (SNR) poses a challenge when constructing EEG-based BCI systems. Moreover, the non-stationary and nonlinear signal issues, the low-spatial data resolution, and the inter- and intra-subject variability hamper the extraction of discriminant features. Indeed, subjects with poor motor skills have difficulties in practicing MI tasks against low SNR scenarios. Here, we propose a subject-dependent preprocessing approach that includes the well-known Surface Laplacian Filtering and Independent Component Analysis algorithms to remove signal artifacts based on the MI performance. In addition, power- and phase-based functional connectivity measures are studied to extract relevant and interpretable patterns and identify subjects of inefficency. As a result, our proposal, Subject-dependent Artifact Removal (SD-AR), improves the MI classification performance in subjects with poor motor skills. Consequently, electrooculography and volume-conduction EEG artifacts are mitigated within a functional connectivity feature-extraction strategy, which favors the classification performance of a straightforward linear classifier.

Non-Cooperative Spectrum Sensing Based on Cyclostationary Model of Digital Signals in the Context of Cognitive Radio

This paper addresses the problem of the opportunistic spectrum access in Cognitive Radio. Indeed, most spectrum sensing algorithms suffer from a high computational cost to achieve the detection process. They need a prior knowledge of signal characteristics and present a bad performance in low Signal to Noise Ratio (SNR) environment. The choice of the optimal detection threshold is another issue for these spectrum sensing algorithms. To overcome the limits of spectrum detectors, we propose in this paper, a blind detection method based on the cyclostationary features of communication signals. Our detector evaluates the level of hidden periodicity contained in the observed signal to make decision on the state of a bandwidth. In order to reduce the computational cost, we take advantage of the FFT Accumulation Method to estimate the cyclic spectrum of the observed signal. Then, we generate the Cyclic Domain Profile of the cyclic spectrum which allows us to evaluate the level of the hidden periodicity in the signal. This level of periodicity is quantified through the crest factor of Cyclic Domain Profile, which represents the decision statistic of the proposed detector. We have established the analytic expression of the optimal threshold of the detection and the probability of detection to evaluate the performance of the proposed detector. Simulation results show that the proposed detector is able to detect the presence of a communication signal on a bandwidth in a very low SNR scenario.

Deep Learning-Based Signal-To-Noise Ratio Estimation Using Constellation Diagrams

Signal-to-noise ratio (SNR) estimation is a fundamental task of spectrum management and data transmission. Existing methods for SNR estimation usually suffer from significant estimation errors when SNR is low. This paper proposes a deep learning (DL) based SNR estimation algorithm using constellation diagrams. Since the constellation diagrams exhibit different patterns at different SNRs, the proposed algorithm achieves SNR estimation via constellation diagram recognition, which can be easily handled based on DL. Three DL networks, AlexNet, InceptionV1, and VGG16, are utilized for DL based SNR estimation. Experimental results show that the proposed algorithm always performs well, especially in low SNR scenarios.

Low Complexity Method for Recovering Continuous Phase Modulation Signals with Low Signal-to-noise Ratios

A low computational complexity scheme based on the modified Wigner–Ville distribution (MWVD) is developed for estimating the instantaneous frequency of continuous phase modulation (CPM) signals under low signal-to-noise ratio (SNR) scenario. To simplify the computation of the MWVD, a low order Chebyshev polynomial is chosen as the kernel function for suppressing the cross-terms. The implementation of this instantaneous frequency (IF) estimator is done through the Viterbi algorithm. The simulation results show that our scheme outperforms other estimating methods under low SNR scenario.

High-Resolution Radar Imaging in Low SNR Environments Based on Expectation Propagation

We address the problem of high-resolution radar imaging in low signal-to-noise ratio (SNR) environments in an approximate Bayesian inference framework. First, the probabilistic graphical model is constructed by imposing the sparsity-promoting spike-and-slab prior to the distribution of scattering centers. Then, the model parameters and phase errors are estimated iteratively by expectation propagation (EP) and maximum likelihood (ML) estimation. Compared with the available imaging methods based on the numerical optimization and Bayesian inference, the proposed method has exhibited more flexibility in data representation and better performance in parameter estimation, particularly in sparse-aperture and low SNR scenarios.

The spread spectrum GFDM schemes for integrated satellite-terrestrial communication system

Recently, integrated Satellite-Terrestrial (S-T) communication system, especially the integration of satellite communication with 5G/6G, is regarded as a research hotpot. Future integrated S-T communication systems are demanding a more compatible and robust physical layer waveform. Considering physical layer access waveform design, this paper proposed a novel Spread Spectrum Generalized Frequency Division Multiplexing (SS-GFDM) scheme for integrated S-T communication system. Traditional GFDM has many advantages such as excellent adaptability and low out-of-band (OOB) radiation. However, because of intrinsic inter carrier interference (ICI) and low signal-to-noise ratio (SNR), the multiple access performance is degraded. In this paper, we introduced CDMA technology into GFDM. Two different spread spectrum modes, Cyclic Code Shift Keying (CCSK) soft spread spectrum and Direct Sequence Spread Spectrum (DSSS), are considered and compared in this paper to illustrate the benefits of GFDM-CDMA in low SNR scenario. Moreover, this scheme integrates the slot-ALOHA protocol with GFDM-CDMA, which extends access freedom in frequency, time and code domain. The simulation and analysis results show that the proposed GFDM-CDMA scheme reduces the performance degradation caused by interference. It is effective in typical satellite channel with low complexity. Meanwhile, the peak-average-power-ratio (PAPR) and access performance has been enhanced significantly.

CIAPC algorithm forrandom frequency‐hopping radar

In random frequency-hopping pulse–Doppler radar, the transmitted frequencies are randomly distributed in the given frequency bandwidth. As a result, this kind of radar has the advantage of anti-jamming. However, because of the sidelobe level fluctuating randomly, the sidelobe of a large scatterer can mask the presence of nearby smaller scatterers. The adaptive pulse compression (APC) algorithm has been shown capable of effectively suppressing the range sidelobes, but the performance gain is diminished whenever the return signals have a low signal-to-noise ratio (SNR). Furthermore, moving targets with high speed bring about the range migration problem. To solve these problems, the coherent integration APC (CIAPC) algorithm is proposed. The simulation results indicate that in a low SNR scenario, the CIAPC algorithm can suppress the range sidelobes of large scatterers and can improve detection performance.

Matched steering\xa0vector searching based direction-of-arrival estimation using acoustic vector sensor array

The acoustic vector sensor (AVS) array is a powerful tool for underwater target’s direction-of-arrival (DOA) estimation without any bearing ambiguities. However, traditional DOA estimation algorithms generally suffer from low signal-to-noise ratio (SNR) as well as snapshot deficiency. By exploiting the properties of the minimum variance distortionless response (MVDR) beamformer, a new DOA estimation method basing on matched steering vector searching is proposed in this article. Firstly, attain the rough estimate of the desired DOA using the traditional algorithms. Secondly, set a small angular interval around the crudely estimated DOA. Thirdly, make the view direction vary in the view interval, and for each view direction, calculate the beam amplitude response of the MVDR beamformer, and find the minimum of the amplitude response. Finally, the pseudo-spatial spectrum is achieved, and the accurate estimate of the desired DOA can be obtained through peak searching. Computer simulations verify that the proposed method is efficient in DOA estimation, especially in low SNR and insufficient snapshot data scenarios.

Novel ISAR autofocusing method based on Bayesian inference

To achieve well-focused ISAR imaging with low signal-to-noise ratio (SNR), this study constructs a statistical model and derives an effective method for image autofocusing based on Bayesian inference. Particularly, hierarchical sparse-promoting priors are imposed on the weights, which are conjugate to the likelihood. The method has closed-form solution which facilitates computation. Experimental results have demonstrated the effectiveness of the method in low SNR and short aperture scenarios.

A Multiple-Measurement Vectors Reconstruction Method for Low SNR Scenarios

Multiple-measurement vectors (MMVs) compressed sensing (CS) provides a better reconstruction performance by using the jointly sparse property, but it suffers a severe performance degradation with the signal-to-noise ratio (SNR) decreasing. This brief presents, a novel MMV CS reconstruction method for the low SNR scenarios, called the signal combining-CS (SC-CS). Compared with other state-of-the-art reconstruction methods, the most innovative feature of the SC-CS is its ability of signal reconstruction in a weak SNR scenario. This makes it a promising candidate for many practical applications when the interfering noises are serious. The proposed method adopts the signal combining techniques, which can effectively suppress undesired noise in the combined measurement. In fact, the SC-CS provides a generalized MMV reconstruction framework in which different signal combining and reconstruction techniques can be used to augment this method. This framework also makes a good tradeoff between the reconstruction performance and the computational complexity. Simulation results show the effectiveness of the proposed method compared with the existing reconstruction methods especially for the low SNR scenarios.

Signal Self-nulling Based DOA Estimation Method Using Acoustic Vector Sensor Array

The acoustic vector sensor (AVS) array is a powerful tool for direction of arrival (DOA) estimation of underwater targets. However, traditional DOA estimation algorithms generally suffer from low signal-to-noise ratio (SNR) as well as snapshot deficiency. A novel signal self-nulling based DOA estimation method is proposed. Firstly, achieve the crude estimation of the desired DOA using the traditional algorithms. Secondly, set the observation angular interval around the crudely estimated DOA. Thirdly, make the observation direction vary in the observation angular interval, and for each imaginary DOA, calculate the amplitude response of the minimum variance distortionless response (MVDR) beamformer. Finally, the pseudo spatial spectrum is achieved. Computer simulations verify that the proposed method is efficient in DOA estimation, especially in low SNR and insufficient snapshot data scenarios.

On Radio Frequency Fingerprint Identification for DSSS Systems in Low SNR Scenarios

Radio frequency fingerprint (RFF) is an intrinsic hardware characteristic and has been employed for device identification. Its application in low signal-to-noise-ratio (SNR) has never been explored because its identification performance is greatly affected by the received signal quality. This letter proposes a novel RFF identification scheme for spread spectrum systems in low SNR scenarios. In the scheme, a signal preprocessing method, information data estimation-based stacking algorithm, is proposed, which leverages the repeated spreading sequences and stacks them together to eliminate the noise and interference effect. Simulation results demonstrate that the proposed scheme can achieve 98% identification rate when the received signal SNR is −15 dB and the length of spreading sequence is 1023.