Additive White Gaussian Noise Environment Research Articles

The Research of Intra-Pulse Modulated Signal Recognition of Radar Emitter under Few-Shot Learning Condition Based on Multimodal Fusion

Radar radiation source recognition is critical for the reliable operation of radar communication systems. However, in increasingly complex electromagnetic environments, traditional identification methods face significant limitations. These methods often struggle with high noise levels and diverse modulation types, making it difficult to maintain accuracy, especially when the Signal-to-Noise Ratio (SNR) is low or the available training data are limited. These difficulties are further intensified by the necessity to generalize in environments characterized by a substantial quantity of noisy, low-quality signal samples while being constrained by a limited number of desirable high-quality training samples. To more effectively address these issues, this paper proposes a novel approach utilizing Model-Agnostic Meta-Learning (MAML) to enhance model adaptability in few-shot learning scenarios, allowing the model to quickly learn with limited data and optimize parameters effectively. Furthermore, a multimodal fusion neural network, DCFANet, is designed, incorporating residual blocks, squeeze and excitation blocks, and a multi-scale CNN, to fuse I/Q waveform data and time–frequency image data for more comprehensive feature extraction. Our model enables more robust signal recognition, even when the signal quality is severely degraded by noise or when only a few examples of a signal type are available. Testing on 13 intra-pulse modulated signals in an Additive White Gaussian Noise (AWGN) environment across SNRs ranging from −20 to 10 dB demonstrated the approach’s effectiveness. Particularly, under a 5−way5−shot setting, the model achieves high classification accuracy even at −10dB SNR. Our research underscores the model’s ability to address the key challenges of radar emitter signal recognition in low-SNR and data-scarce conditions, demonstrating its strong adaptability and effectiveness in complex, real-world electromagnetic environments.

Channel-Blind Joint Source-Channel Coding for Wireless Image Transmission.

Joint source-channel coding (JSCC) based on deep learning has shown significant advancements in image transmission tasks. However, previous channel-adaptive JSCC methods often rely on the signal-to-noise ratio (SNR) of the current channel for encoding, which overlooks the neural network's self-adaptive capability across varying SNRs. This paper investigates the self-adaptive capability of deep learning-based JSCC models to dynamically changing channels and introduces a novel method named Channel-Blind JSCC (CBJSCC). CBJSCC leverages the intrinsic learning capability of neural networks to self-adapt to dynamic channels and diverse SNRs without relying on external SNR information. This approach is advantageous, as it is not affected by channel estimation errors and can be applied to one-to-many wireless communication scenarios. To enhance the performance of JSCC tasks, the CBJSCC model employs a specially designed encoder-decoder. Experimental results show that CBJSCC outperforms existing channel-adaptive JSCC methods that depend on SNR estimation and feedback, both in additive white Gaussian noise environments and under slow Rayleigh fading channel conditions. Through a comprehensive analysis of the model's performance, we further validate the robustness and adaptability of this strategy across different application scenarios, with the experimental results providing strong evidence to support this claim.

Channel Estimation for Underwater Acoustic Communications in Impulsive Noise Environments: A Sparse, Robust, and Efficient Alternating Direction Method of Multipliers-Based Approach

Channel estimation in Underwater Acoustic Communication (UAC) faces significant challenges due to the non-Gaussian, impulsive noise in ocean environments and the inherent high dimensionality of the estimation task. This paper introduces a robust channel estimation algorithm by solving an l1−l1 optimization problem via the Alternating Direction Method of Multipliers (ADMM), effectively exploiting channel sparsity and addressing impulsive noise outliers. A non-monotone backtracking line search strategy is also developed to improve the convergence behavior. The proposed algorithm is low in complexity and has robust performance. Simulation results show that it exhibits a small performance deterioration of less than 1 dB for Channel Impulse Response (CIR) estimation in impulsive noise environments, nearly matching its performance under Additive White Gaussian Noise (AWGN) conditions. For Delay-Doppler (DD) doubly spread channel estimation, it maintains Bit Error Rate (BER) performance comparable to using ground truth channel information in both AWGN and impulsive noise environments. At-sea experimental validations for channel estimation in Orthogonal Frequency Division Multiplexing (OFDM) systems further underscore the fast convergence speed and high estimation accuracy of the proposed method.

A Novel Concatenated LWT and WHT Based UFMC Waveform Design for the Next Generation Wireless Communication Systems

A novel concatenated Lifting Wavelet Transform (LWT) and Walsh Hadamard Transform (WHT) with Universal Filtered Multi Carrier (UFMC) waveform is suggested as an alternative to Orthogonal Frequency Division Multiplexing (OFDM) waveform for the next generation wireless telecommunication systems in this paper. The suggested waveform is composed of LWT and WHT with UFMC waveform. Both the elimination the disadvantages of OFDM and the improvement the achievement of UFMC waveform, from the fifth generation waveform candidates, has been aimed with the suggested waveform. In order to verify the performance of the suggested waveform and compare with the traditional OFDM, UFMC, and LWT‐UFMC waveform computer simulation studies are performed in additive white Gaussian noise and flat Rayleigh fading channel environments. The acquired computer simulation results exhibit that the achievement of the suggested waveform has better than the achievement of the traditional OFDM, UFMC, and LWT‐UFMC waveform. © 2021 Institute of Electrical Engineers of Japan. Published by Wiley Periodicals LLC.

Walsh‐Hadamard‐transform‐based SC‐FDMA system using WARP hardware

Single-carrier frequency division multiple access (SC-FDMA) is currently being used in long-term evolution uplink communications owing to its low peak-to-average power ratio (PAPR). This study proposes a new transceiver design for an SC-FDMA system based on Walsh-Hadamard transform (WHT). The proposed WHT-based SC-FDMA system has low-PAPR and better bit-error rate (BER) performance compared with the conventional SC-FDMA system. The WHT-based SC-FDMA transmitter has the same complexity as that of discrete Fourier transform (DFT)-based transmitter, while the receiver's complexity is higher than that of the DFT-based receiver. The exponential companding technique is used to reduce its PAPR without degrading its BER. Moreover, the performances of different ordered WHT systems have been studied in additive white Gaussian noise and multipath fading environments. The proposed system has been verified experimentally by considering a real-time channel with the help of wireless open-access research platform hardware. The supremacy of the proposed transceiver is demonstrated based on simulated and experimental results.

Maximum Rate Scheduling With Adaptive Modulation in Mixed Impulsive Noise and Additive White Gaussian Noise Environments

This article proposes an opportunistic scheduling scheme based on adaptive modulation for users in a mixed noise environment, where some users are under additive white Gaussian noise (AWGN) and other users are exposed to impulsive noise. Unlike the scenario, where all the users are in an AWGN environment, the maximum signal-to-noise ratio (SNR) scheduler does not provide the maximum capacity if users are in a mixed noise environment. In the proposed scheduling scheme, called maximum rate scheduler, the user with the highest rate (or highest modulation order), instead of highest SNR, is selected. To evaluate the performance of the proposed scheduling scheme, the analyses of average spectral efficiency, outage probability and system capacity are provided along with a simple calculation of SNR thresholds for adaptive modulation in an impulsive noise environment satisfying target symbol error rate (SER). Simulation results illustrate that system capacity with the proposed scheduling given target SER = 10 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">-2</sup> and the average spectral efficiency are improved by 156% and 124%, respectively, at SNR = 5 dB when a strong impulsive noise is present.

Performance Analysis of Sliding Window Multiuser Detectors Used in Asynchronous Ds-cdma Systems (Dept.E)

In this paper, the sliding-window method is applied on both decorrelating and linear minimum mean-squared error (LMMSE) multiuser detectors. The analytical expressions for the error probability of those multiuser detectors (MUDs) are derived. The analysis is presented for asynchronous direct sequence code-division multiple access (DS-CDMA) in additive white Gaussian noise (AWGN) and multipath fading environment. The adopted detectors divide the received signal stream into processing windows and process them window by window. The effects of expanding the processing-window length on the error probabilities are investigated. The analysis shows that the error performance of the decorrelating detector is much better than that of the LMMSE detector when the power of the interfering users is larger than the desired user power. It is also shown that the performance of the presented detectors will be significantly improved with processing-window length up to four symbols.

Threshold selection analysis of spectrum sensing for cognitive radio network with censoring based imperfect reporting channels

An appropriate threshold selection scheme is one of the main components to adjudicate the performance of energy detection spectrum sensing (EDSS) technique for cognitive radio network. In this paper, we have employed two different threshold selection approaches namely, the constant false-alarm rate (CFAR) and minimized error probability (MEP) and analyzed the threshold selection effects on the performance of cognitive user (CU) communication systems particularly, the total spectrum sensing error probability and throughput. We have derived the expressions and analyzed these performance parameters by considering an imperfect spectrum sensing and reporting channels in the cooperative spectrum sensing scenarios for additive white Gaussian noise (AWGN), Rayleigh and Nakagami-m fading environments. In addition, the censoring concept has been applied to the proposed system and compared its effect with that of the non-censoring based cognitive radio network (CRN) system under the perfect reporting (PR) and imperfect reporting (IR) channel. With the help of simulation, we have illustrated that the role of threshold selection approach is crucial to maximize the throughput and minimize the spectrum sensing error while considering the amount of error in the reporting channel. Further, from the results, the existence of trade-off between the spectrum sensing error probability and throughput is presented with threshold selection approaches. Moreover, it is also shown that there is need to switch among CFAR and MEP threshold selection approaches in the censoring scenario, to enhance the throughput and decrease the spectrum sensing error probability.

Method for Classifying a Noisy Raman Spectrum Based on a Wavelet Transform and a Deep Neural Network

Because it is relatively difficult in practice to classify the Raman spectrum under baseline noise and additive white Gaussian noise environments, this paper proposes a new framework based on a wavelet transform and deep neural network for identification of noisy Raman spectra. The framework consists of two main engines. Wavelet transform is proposed as the framework front end for transforming the 1-D noise Raman spectrum to two-dimensional data. The two-dimensional data are fed to the framework back end, which is a classifier. The optimum classifier is chosen by implementing several traditional machine learning (ML) and deep learning (DL) algorithms, and we investigate their classification accuracy and robustness performances. The four chosen MLs are naive Bayes (NB), a support vector machine (SVM), a random forest (RF) and a k-nearest neighbor (KNN), and a deep convolution neural network (DCNN) was chosen as a DL classifier. Noise-free, Gaussian noise, baseline noise, and mixed-noise Raman spectra were applied to train and validate the ML and DCNN models. The optimum back-end classifier was obtained by testing the ML and DCNN models with several noisy Raman spectra (10-30 dB noise power). Based on the simulation, the accuracy of the DCNN classifier is 9% higher than that of the NB classifier, 3.5% higher than the RF classifier, 1% higher than the KNN classifier, and 0.5% higher than the SVM classifier. In terms of robustness to mixed noise scenarios, the framework with the DCNN back end showed superior performance compared with the other ML back ends. The DCNN back end achieved 90% accuracy at 3 dB SNR, while the NB, SVM, RF, and K-NN back ends required 27 dB, 22 dB, 27 dB, and 23 dB SNR, respectively. In addition, in the low-noise test dataset, the F-measure score of the DCNN back end exceeded 99.1%, and the F-measure scores of the other ML engines were below 98.7%.

An Iterative Extended Kalman Filter for Coherent Measurements of Incoherent Network Nodes in Positioning Systems

Many positioning and tracking applications use spatially distributed sensor stations, each equipped with coherent measurement channels. The coherent data set of each node is incoherently measured to the data sets of all other nodes, avoiding expensive synchronization procedures between the stations. Usually, the measurements are evaluated by mapping the incoherently measured complex valued data on real valued data like angle of arrival or received signal strength. After this preprocessing step, recursive filters fuse the real valued data to estimate a system state. Unfortunately, even though the original measurements are performed in additive white Gaussian noise environments, the preprocessing step can result in correlated, noise shaped errors, whose variance is system state dependent. Hence, the additive white Gaussian noise assumption, which is commonly drawn in Kalman filters, is violated. Therefore, this paper proposes an iterative extended Kalman filter, which estimates the state of a nonlinear real valued system via a complex valued measurement model that consists of incoherent sensor stations, each with several coherent measurement channels. Since the proposed iterative extended Kalman filter is well suited for distributed positioning systems with several stations, a transmitter is localized in a simulation via two sensor stations, each measuring the received signal's amplitude and phase at four channels. To illustrate the advantages of the proposed algorithm, the direct measurement evaluation using the proposed algorithm is compared to a Kalman filter that evaluates the received signal strengths and angle of arrivals at each sensor station. Finally, a reflecting wall is incorporated into the simulation scenario to demonstrate the flexibility of the proposed Kalman filter.

Concatenation of polar codes with three‐dimensional parity check (3D‐PC) codes to improve error performance over fading channels

SummaryIn this paper, we concatenated of three‐dimensional parity check (3D‐PC) block and polar codes for improving error correction performance and bit error rate (BER). Three different sizes of 3D parity check blocks (4 × 4 × 4, 8 × 8 × 8, and 16 × 16 × 16) are used for polar code concatenation. The 4 × 4 × 4 block returns the best performance, but higher complexity of decoder is needed unlikely. The 8 × 8 × 8 has returned acceptable complexity and good performacne. The complexity of decoder is less in the case of 16 × 16 × 16 with slight performance. The performance of the 3D‐PC is reduced when the codewords length is increased. The experiment considered the presence of additive white Gaussian noise (AWGN) with Rayleigh and Rician fading environments. 3D‐PC and polar code concatenation is more precise with codewords of short length, whereas there is insufficient concatenation accuracy with longer codewords. The outcomes of this study contain comparison between AWGN, Rayleigh, and Rician environments. The AWGN is noticed to have a lesser negative impact on the performance of code. Furthermore, increasing the code length may slightly fill the gap of performance between the concatenated and none concatenated polar codes due to the impact of code length on parity check code performance. Simulation results showed the coding performance in case of the polar code with concatenation and without concatenation for different code lengths. Generally, the 3D‐PC polar code concatenation is drawn the optimal result in AWGN environments.

Sliding Residual Network for High-Speed Target Detection in Additive White Gaussian Noise Environments

Facing high-speed targets, the traditional radar target detection framework may suffer from performance degradation due to the signal-to-noise ratio loss caused by Doppler mismatches, especially for phase coded waveforms. In this paper, an end-to-end sliding residual network detector (SRND), which is derived from the likelihood ratio test, is proposed to detect high-speed targets in additive white Gaussian noise environments with a single radar echo pulse. The SRND uses a residual network with an efficient depth to increasingly capture the representations of target echoes, and we partially show this process through visualization. The SRND is robust to target velocities because the employed residual network utilizes layers of convolutional filters to match with target echoes of both low-speed and high-speed targets. Besides, with a waveform adapter, the SRND is compatible with different waveforms, that is to say, the SRND needs to be trained only once and then can cope with different phase modulations of waveforms. More importantly, the SRND, which is trained with computer-generated data only, can deal with not only simulated data but also measured data. Numerical experiments are given to demonstrate the superior detection performance of the SRND over the traditional detector.

Improved Eigenvalue Decomposition-Based Approach for Reducing Cross-Terms in Wigner–Ville Distribution

In this work, a novel data-driven methodology is proposed to reduce cross-terms in the Wigner–Ville distribution (WVD) using improved eigenvalue decomposition of the Hankel matrix (IEVDHM). The IEVDHM method decomposes a multi-component non-stationary (NS) signal into mono-component NS signals. After that, amplitude-based segmentation is applied to obtain the components which are separated in time domain. Further, frequency modulation (FM) rate of components is observed to achieve an adaptive window. The adaptive window successfully removes intra-cross-terms which are generated due to nonlinearity present in FM. Finally, the sum of WVD of all the components is considered the WVD of the multi-component NS signal. The simulation study has been carried out on synthetic and natural signals to show the effectiveness of the proposed method. Performance of the proposed method is compared with the existing methods. We have also evaluated the performance of the proposed method in additive white Gaussian noise environment. The normalized Renyi entropy measure is computed to show the efficacy of the proposed method and all the compared methods for obtaining time–frequency representation.

Standard-independent I/Q imbalance estimation and compensation scheme inOFDM

Direct-conversion transceivers are gaining increasing attention due to their low power consumption. However, they suffer from a serious in- and quadrature-phase (I/Q) imbalance problem. The I/Q imbalance can severely limit the achievable operating signal-to-noise ratio (SNR) at the receiver and, consequently, the supported constellation sizes and data rates. In this paper, we first investigate the effects of I/Q imbalance on orthogonal frequency division multiplexing (OFDM) receivers, and then propose a new I/Q imbalance compensation scheme. In the proposed method, a new statistic, which is robust against channel distortion, is used to estimate the I/Q imbalance parameters, and then the I/Q imbalance is corrected in the frequency domain. Simulations are performed to verify the effectiveness of the proposed method for I/Q imbalance compensation. The results show that the proposed I/Q imbalance compensation method can achieve bit error rate (BER) performance close to that in the ideal case without I/Q imbalance in additive white Gaussian noise (AWGN) or multipath environments. Furthermore, because no pilot information is required, this method can be applied in various standard communication systems.

Positioning performance analysis of the time sum of arrival algorithm with error features

The theoretical positioning accuracy of multilateration (MLAT) with the time difference of arrival (TDOA) algorithm is very high. However, there are some problems in practical applications. Here we analyze the location performance of the time sum of arrival (TSOA) algorithm from the root mean square error (RMSE) and geometric dilution of precision (GDOP) in additive white Gaussian noise (AWGN) environment. The TSOA localization model is constructed. Using it, the distribution of location ambiguity region is presented with 4-base stations. And then, the location performance analysis is started from the 4-base stations with calculating the RMSE and GDOP variation. Subsequently, when the location parameters are changed in number of base stations, base station layout and so on, the performance changing patterns of the TSOA location algorithm are shown. So, the TSOA location characteristics and performance are revealed. From the RMSE and GDOP state changing trend, the anti-noise performance and robustness of the TSOA localization algorithm are proved. The TSOA anti-noise performance will be used for reducing the blind-zone and the false location rate of MLAT systems.

New 3D 16‐Ary Signal Constellations and Their Symbol Error Probabilities in AWGN and Rayleigh Fading Channels

Two three‐dimensional (3D) 16‐ary signal constellations having extended lattice structures are presented in this paper. The theoretical symbol error probabilities (SEPs) of the new constellations in additive white Gaussian noise (AWGN) and Rayleigh fading channel are derived. Computer simulation confirms that the closed‐form expressions for average SEPs of the constellations in the AWGN channel are very accurate. The theoretical SEP upper bounds in the Rayleigh fading channel are very tight. Since the presented constellations have larger minimum Euclidean distance (MED) than the conventional one, error performance can be improved up to 0.9 dB in an AWGN environment. Hence, the proposed constellations and their theoretical analysis can be used as a reference for the development of a wireless communication system with 3D signal constellations.

Blind signal modulation recognition through clustering analysis of constellation signature

Modulation classification using OPTICS clustering algorithm has been proposed.Algorithm is reliably classifying 4ASK, 8ASK, BPSK, QPSK and 8QAM above 9dB SNR.Classification is unsupervised and generalized for any orders ASK, PSK and QAM.Algorithm is implemented and tested on real time RF signal using labVIEW. Automatic recognition of digital modulation schemes is becoming an active research area in many covert operations. It has many military applications where surveillance and electronic warfare requires a type of modulation in intercepted signal to prepare jamming signals. Most of the approaches are based on modulated signal's component, but the modulation type can be best identified with the use of constellation diagram. The proposed technique is able to recognize M-QAM, M-ASK, and M-PSK modulation scheme in Additive White Gaussian Noise (AWGN) environment. As the constellation points form clusters in the I-Q plane, the order of the modulation can be obtained by estimating the correct number of clusters, which is calculated by OPTICS algorithm. The least square error has been calculated using linear regression from the obtained constellation points, to identify either ASK or PSK and QAM. The error is least for ASK which differentiates ASK from PSK and QAM. To identify between the PSK and QAM, k-means clustering is employed to find the number of centroids equal to order of modulation estimated by OPTICS. With the difference in maximum and minimum absolute value of the centroids, PSK or QAM is recognized. The proposed method shows an improvement in the classification accuracy which reaches 100% using 1024 symbols at 20dB compared to 98.89%, 98.05%, and 98% when using more complex classifiers like Support Vector Machine, Naive Bayes Classifier, KNN respectively. The method used is unsupervised whereas most of the methods in the literature require training phase to set the thresholds or weights for final model to detect modulation type. This algorithm is also implemented in LabVIEW, and tested on real-time signals. An intelligent system is made which does not require any knowledge of symbol rate, carrier frequency, and any training phase to set thresholds, and detects the type of modulation blindly in real time. Modulated RF signals are generated by NI PXIe-5673 (RF transmitter). NI PXI 5600 is used to downconvert RF signal and NI PXI-5142 (100 MS/s OSP digitizer) is used to sample the downverted signal.

Analysis and Design of an Energy Efficient Differential Coherent Chaos Based System in Additive White Gaussian Noise Environment

A new differential coherent chaos based scheme is proposed and named as Differential Chaos ON-OFF Keying (DCOOK). The proposed scheme provides reduction in bit energy and better bit error performance at large spreading factor. This is achieved by presenting each transmitted bit by either identical segment or no transmission within same bit duration. The receiver performs simple correlation between the received signal and its delayed version to determine the transmitted information utilizing the low correlation between the noise signals. The bit error rate (BER) performance of the proposed scheme is evaluated analytically using Gaussian Approximation (GA) method and compared with the simulation results. The results show excellent agreement between the derived expression and simulation. Moreover, the BER of DCOOK scheme is compared with the standard chaos systems: Chaos ON-OFF Keying (COOK), Differential Chaos Shift Keying (DCSK), and Correlation Delay Shift Keying (CDSK). The comparison results show that DCOOK scheme can always achieve superior performance compared to COOK and CDSK schemes and even exceeds the performance of DCSK scheme at typical spreading factor values.

Automatic modulation classification based on high order cumulants and hierarchical polynomial classifiers

In this paper, a Hierarchical Polynomial (HP) classifier is proposed to automatically classify M-PSK and M-QAM signals in Additive White Gaussian Noise (AWGN) and slow flat fading environments. The system uses higher order cumulants (HOCs) of the received signal to distinguish between the different modulation types. The proposed system divides the overall modulation classification problem into several hierarchical binary sub-classifications. In each binary sub-classification, the HOCs are expanded into a higher dimensional space in which the two classes are linearly separable. It is shown that there is a significant improvement when using the proposed Hierarchical polynomial structure compared to the conventional polynomial classifier. Moreover, simulation results are shown for different block lengths (number of received symbols) and at different SNR values. The proposed system showed an overall improvement in the probability of correct classification that reaches 100% using only 512 received symbols at 20 dB compared to 98% and 98.33% when using more complicated systems like Genetic Programming with KNN classifier (GP-KNN) and Support Vector Machines (SVM) classifiers, respectively.

PAPR Reduction in OFDM Signals: An Adaptive-Network-Based Fuzzy Inference Approach

In this paper, an adaptive-network-based fuzzy inference system (ANFIS) based scheme is analyzed and proposed for reducing the peak-to-average power ratio (PAPR) in multicarrier signals under additive white Gaussian noise and multipath fading (Raleigh) channel environment. This scheme involves training of ANFIS structure in time domain using Orthogonal Frequency Division Multiplexing signals with low PAPR, such as those obtained by approximate gradient project---null subcarrier switching (AGP---NCS) method. Once the ANFIS module is trained, the proposed scheme approximately offers similar reduction in PAPR as compared to AGP---NCS method, with significantly less convergence time and computational complexity. he results show that proposed approach is not only less complex but also maintains the data rate and bit error rate performance compared with other conventional schemes.