Analysis Filter Bank Research Articles

New two-microphone simplified sub-band forward algorithm based on separated variable step-sizes for acoustic noise reduction

This paper presents an extended two-microphone adaptive filtering algorithm implemented on the sub-band simplified forward technique using new separated variable step-sizes parameters (denoted SSF-SVS: Simplified Sub-band Forward Separated Variable Step-sizes). This algorithm is proposed to reduce the acoustic noise and enhance the quality of estimated speech signal. Firstly, a sub-signal decomposition processing strategy is used to facilitating effective noise reduction with very fast convergence rate using two analysis filter banks. Secondly, a two-microphone simplified forward blind source separation (BSS) structure updated by the normalized least-mean-square algorithm (NLMS) for extracting the estimated speech signal. To guarantee a very fast convergence rate with high level of enhanced speech quality, thirdly we proposed new separated variable step-sizes parameters for controlling the adaptation of sub-band filters. The concept involves suggesting individual variable step-size parameter for each adaptive sub-filter, which are updated independently using the input sub-signal and the estimated output sub-signal. Experimental results confirm significant improvements in terms of convergence rate based on the mean square error criterion (MSE) in diverse acoustical environments. In other hand, the good performance of the proposed SSF-SVS algorithm in terms of speech quality is verified by on output signal-to-noise ratio (Output-SNR). The proposed algorithm compared with six two-microphone adaptive filtering algorithms in term of convergence rate, speech quality and computational complexity.

NNMF with Speaker Clustering in a Uniform Filter-Bank for Blind Speech Separation

This study proposes a blind speech separation algorithm that employs a single-channel technique. The algorithm’s input signal is a segment of a mixture of speech for two speakers. At first, filter bank analysis transforms the input from time to time-frequency domain (spectrogram). Number of sub-bands for the filter is 257. Non-Negative Matrix Factorization (NNMF) factorizes each sub-band output into 28 sub-signals. A binary mask separates each sub-signal into two groups; one group belongs to the first speaker and the other to the second speaker. The binary mask separates each sub-signal of the (257×28) 7196 sub-speech signals. That separation cannot identify the speaker. Identification of the sub-signal speaker for each sub-signal is achieved by speaker clustering algorithms. Since speaker clustering cannot process without speaker segmentation, the standard windowed-overlap frames have been used to partition the speech. The speaker clustering process fetches the extracted phase angle from the spectrogram (of the mixture speech) and merges it into the spectrogram (of the recovered speech). Filter bank synthesizes these signals to produce a full-band speech signal for each speaker. Subjective tests denote that the algorithm results are accepted. Objectively, the researchers experimented with 66 mixture chats (6 females and 6 males) to test the algorithm. The average of the SIR test is 11.1 dB, SDR is 1.7 dB, and SAR is 2.8 dB.

Filter bank second-order underdamped stochastic resonance analysis for implementing a short-term high-speed SSVEP detection

ObjectiveThe progression of brain-computer interfaces (BCIs) has been propelled by breakthroughs in neuroscience, signal processing, and machine learning, marking it as a dynamic field of study over the past few decades. Nevertheless, the nonlinear and non-stationary characteristics of steady-state visual evoked potentials (SSVEPs), coupled with the incongruity between frequently employed linear techniques and nonlinear signal attributes, resulted in the subpar performance of mainstream non-training algorithms like canonical correlation analysis (CCA), multivariate synchronization index (MSI), and filter bank CCA (FBCCA) in short-term SSVEP detection. MethodsTo tackle this problem, the novel fusions of common filter bank analysis, CCA dimensionality reduction methods, USSR models, and MSI recognition models are used in SSVEP signal recognition. ResultsUnlike conventional linear techniques such as CCA, MSI, and FBCCA, the filter bank second-order underdamped stochastic resonance (FBUSSR) analysis demonstrates superior efficacy in the detection of short-term high-speed SSVEPs. ConclusionThis research enlists 32 subjects and uses a public dataset to assess the proposed approach, and the experimental outcomes indicate that the non-training method can attain greater recognition precision and stability. Furthermore, under the conditions of the newly proposed fusion method and light stimulation, the USSR model exhibits the most optimal enhancement effect. SignificanceThe findings of this study underscore the expansive potential for the application of BCI systems in the realm of neuroscience and signal processing.

Compensating Chromatic Dispersion and Phase Noise using Parallel AFB-MBPS For FBMC-OQAM Optical Communication System

Filter Bank Multi-Carrier Offset-QAM (FBMC-OQAM) is one of the hottest topics in research for 5G multi-carrier methods because of its high efficiency in the spectrum, minimal leakage in the side lobes, zero cyclic prefix (CP), and multiphase filter design. Large-scale subcarrier configurations in optical fiber networks need the use of FBMC-OQAM. Chromatic dispersion is critical in optical fiber transmission because it causes different spectral waves (color beams) to travel at different rates. Laser phase noise, which arises when the phase of the laser output drifts with time, is a major barrier that lowers throughput in fiber-optic communication systems. This deterioration may be closely related among channels that share lasers in multichannel fiber-optic systems using methods like wavelength-division multiplexing with frequency combs or space-division multiplexing. In this research, we use parallel Analysis Filter Bank (AFB) equalizers in the receiver part of the FBMC OQAM Optical Communication system to compensate for chromatic dispersion (CD) and phase noise (PN). Following the equalization of CD compensation, the phase of the carriers in the received signal is tracked and compensated using Modified Blind Phase Search (MBPS). The CD and PN compensation techniques are simulated and analyzed numerically and graphically to determine their efficacy. To evaluate the FBMC's efficiency across various equalizers, 16-OQAM is taken into account. Bit Error Rate (BER), Optical Signal-to-Noise Ratio (OSNR), Q-Factor, and Mean Square Error (MSE) were the primary metrics we utilized to evaluate performance. Single-tap equalizer, multi-tap equalizer (N=3), ISDF equalizer with suggested Parallel Analysis Filter Banks (AFBs) (K=3), and MBPS were all set aside for comparison. When compared to other forms of Nonlinear compensation (NLC), the CD and PN tolerance attained by Parallel AFB equalization with MBPS is the greatest.

A novel optimal wavelet filter banks for automated diagnosis of Alzheimer’s disease and mild cognitive impairment using Electroencephalogram signals

Electroencephalogram (EEG) of Alzheimer’s disease (AD) patients show a slowing effect and less synchronization. EEG signal’s transient and abrupt nature is captured from various mother wavelets. However, better performance can be obtained by balancing time–frequency localization in wavelet filters. We propose a new approach for designing filter banks based on optimal time–frequency localization of a four-step lifting structure (FSLS). First, we design a FSLS using Euler’s Frobenius half-band polynomial (EFHBP). The perfect reconstruction condition and vanishing moments are imposed on EFHBP to achieve maximum flat half-band filters (HBFs). HBFs are optimized using a balanced uncertainty metric to obtain a time–frequency spread balance. Afterward, these HBFs are used in the FSLS to obtain the synthesis and analysis filter banks. The proposed time–frequency optimized biorthogonal wavelet filter banks (TFOBWFBs) achieved a balance between time–frequency localization. Further, these TFOBWFBs are applied to decompose the EEG signals of AD patients. Twenty different features were extracted from decomposed EEG subbands, of which twelve significant features were selected using Kruskal Walli’s test. The machine learning models were trained and tested using obtained features with 10-fold cross-validation and leave-one-subject-out cross-validation. To validate this study, the proposed TFOBWFBs have been applied to two publicly available EEG datasets of mild cognitive impairment (MCI), AD, and healthy control (HC) subjects. The proposed TFOBWFBs achieved 98.90% accuracy for 2-way (AD vs. HC) and 96.50% for 3-way (AD vs. MCI vs. HC) classification using a support vector machine model with a 10-fold cross-validation. The proposed method outperforms the existing AD and MCI detection techniques. The performance of the proposed machine learning framework with optimization of wavelet filter banks is more significant and fast compared to previous studies. Also, the proposed framework can be applied to detect other neurodegenerative disorders.

Research on phase modulation to enhance the feature of high-frequency steady-state asymmetric visual evoked potentials

High-frequency steady-state asymmetric visual evoked potential (SSaVEP) provides a new paradigm for designing comfortable and practical brain-computer interface (BCI) systems. However, due to the weak amplitude and strong noise of high-frequency signals, it is of great significance to study how to enhance their signal features. In this study, a 30 Hz high-frequency visual stimulus was used, and the peripheral visual field was equally divided into eight annular sectors. Eight kinds of annular sector pairs were selected based on the mapping relationship of visual space onto the primary visual cortex (V1), and three phases (in-phase[0º, 0º], anti-phase [0º, 180º], and anti-phase [180º, 0º]) were designed for each annular sector pair to explore response intensity and signal-to-noise ratio under phase modulation. A total of 8 healthy subjects were recruited in the experiment. The results showed that three annular sector pairs exhibited significant differences in SSaVEP features under phase modulation at 30 Hz high-frequency stimulation. And the spatial feature analysis showed that the two types of features of the annular sector pair in the lower visual field were significantly higher than those in the upper visual field. This study further used the filter bank and ensemble task-related component analysis to calculate the classification accuracy of annular sector pairs under three-phase modulations, and the average accuracy was up to 91.5%, which proved that the phase-modulated SSaVEP features could be used to encode high- frequency SSaVEP. In summary, the results of this study provide new ideas for enhancing the features of high-frequency SSaVEP signals and expanding the instruction set of the traditional steady state visual evoked potential paradigm.

Interpolated Individual Weighting Subband Volterra Filter for Nonlinear Active Noise Control

Nonlinear active noise control (NLANC) is always a challenge in algorithm design. Most of the NLANC algorithms are based on the linear-in-the-parameters (LIP) filters, and no work is conducted on subband adaptive filter (SAF). In this brief, a novel nonlinear SAF, termed the Volterra filtered-x normalized SAF (VFxNSAF), is proposed for NLANC, whose input vector is obtained according to the size of the analysis filter bank. Moreover, the virtual microphone strategy is applied to VFxNSAF to suppress the noise at a remote location. To mitigate the computational burden, the interpolated IWVFxNSAF with individual weighting (IW) (I-IWVFxNSAF) is proposed, in which the quadratic kernel is obtained by the interpolated filter. Simulation results corroborate the superiority of the VFxNSAF and I-IWVFxNSAF algorithms for NLANC.

Enhancing P300 Detection Using a Band-Selective Filter Bank for a Visual P300 Speller

Background: An open challenge of P300-based BCI systems focuses on recognizing ERP signals using a reduced number of trials with enough classification rate.Methods: Three novel methods based on Filter Bank and Canonical Correlation Analysis (CCA) are proposed for the recognition of P300 ERPs using a reduced number of trials. The proposed methods were evaluated with two freely available EEG datasets based on 6x6 speller and were compared with five standard methods: Mean-Amplitude, Step-Wise, Principal Component Analysis, Peak, and CCA.Results: The proposed methods outperform significantly standard algorithms for P300 identification with a maximum AUC of 0.93 and 0.98, and an average of 0.73 and 0.76, using a single trial.Conclusion: Proposed methods based on Filter Bank are robust for the identification of P300 using a reduced number of trials, which could be used in real-time BCI spellers for rehabilitation engineering.

Filterbank-Assisted Channel Estimation for Coherent Optical FBMC/OQAM System

In this paper, a new channel estimator for optical filter bank multicarrier with an offset-QAM modulation (FBMC/OQAM) system is proposed. The conventional channel estimation techniques that are based on interference approximation method (IAM) preamble with least squares (LS) approach suffers from sensitivity to intrinsic imaginary interference (IMI). Therefore, a filterbank-assisted (FB) technique is proposed to eliminate the residual interference associated with the preamble to improve the performance of optical FBMC/OQAM system. To further enhance the system performance, a thresholding block is added between the synthesis and analysis filter banks at the receiver to suppress the residual interference. Simulation findings show that the proposed channel estimator is more effective than the conventional IAM-R and IAM-C with LS based channel estimation at estimating the optical channel that includes chromatic dispersion and residual IMI.

Subband Decomposition Based Output-Only Modal Analysis

AbstractOutput-only modal analysis (OMA) is an indispensable alternative to experimental modal analysis for engineering structures while in operation. Conventional OMA often fails to identify the underlying modal structure with insufficient modal energy contribution. Such low modal participation is expected when the sampled response is subjected to sensor nonlinearity or when specific modes are not directly excited. A novel subband decomposition (SBD) method that resolves modal parameters even with biased modal energy distribution is proposed. It isolates the system response within a narrow frequency subband through a finite impulse response analysis filter bank. Whenever the filter subband captures a resonance, the filtered system response is close-to-singular and contains mainly the resonant mode contribution. A modal cluster metric is defined to identify the resonant normal modes automatically. The modal parameters are also identified and extracted within the subband possessing the locally maximal clustering measure. The proposed method assumes no a priori knowledge of the structure under operation other than the system should have any repeated natural frequencies. Therefore, the SBD algorithm is entirely data-driven and requires minimal user intervention. To illustrate the concept and the accuracy of the proposed SBD, numerical experiments of a linear cantilevered beam with various stationary and non-stationary loading are conducted and compared to other OMA methods. Furthermore, physical experiments on an aluminum cantilever beam examine the method’s applicability in field modal testing. Compared to traditional OMA methods, the numerical and physical experiments show orders of magnitude improvement in modal identification error using the proposed SBD.

Jarvis: AI Virtual Assistant

"Jarvis" was the protagonist of Tony's Stark starring in the movie Iron Man. Unlike the first jokes when Jarvis was Stark's servant, Jarvis' movie version is a smart computer that communicates critical issues, monitors his family, and helps build and organize his hero's suit. In this project, Jarvis is a Digital Health Assistant who uses social media i.e., voice that creates two-way communication between people and his personnel Computer, performing various tasks such as calling and chatting on WhatsApp, taking notes, locating, etc. In our project, we use the word just as communication means that Jarvis is a Speech recognition program. The concept of speech technology combines two technologies: Synthesizer and vision. Speech synthesizer captures input and generates audio streams as output this is achieved using the pyttsx3 library accompanied by Speech function. Speech recognition. On the other hand, he did the opposite. It treats audio streaming as input and thus converts it into text writing and this output uses the Speech Recognition library accompanied by the TakeCommand function. The word is a symbol of endless knowledge. Direct analysis and integration of complex voice signal is due to the vast amount of information contained in the signal. Therefore, digital signal processes such as Feature Visibility and Feature Matching are introduced to represent the voice signal. For this project, we are directly using a speech engine that uses an output element such as Mel scaled frequency cepstral. The mel scale frequency cepstral coefficients (MFCCs) obtained from the fourier transform and filter bank analysis are probably the most widely used in high-level speech recognition systems. We aim to create more jobs that can help people in their daily lives and reduce their efforts as in this project we have introduced Chrome and Youtube Automation. In our test, we checked that all of this functionality works well. We test this on 2 speakers (1 female and 1 male) to find the exact motive for the speculation.

Research of application of DFT-modulated filter bank in systems with significant spectral component amplification

The purpose of this article is to investigate the application of DFT-modulated filter bank in systems with significant spectral component amplification like hearing aid. There is a description of analysis / synthesis method based on short-time Fourier transform (STFT), which is used in most systems of speech information processing. It is shown that DFT-modulated filter bank is a generalization of STFT-method. In analysis / synthesis system based on DFT-modulated filter bank, the input signal is divided into subbands, passing through the analysis filter bank then each subband is amplified and the last step is to reconstruct the signal with synthesis filter bank. However, in digital systems with significant spectral component amplification, the resulting signal is distorted after reconstruction because of amplification factor difference in each subband. The article provides expressions for the distortion and the aliasing functions, allowing to estimate the distortion value, which appears in analysis / synthesis system of DFT-modulated filter bank. Efficient algorithms for calculating the distortion and the aliasing functions are also offered. In future it is planning to develop a procedure for optimizing the DFT-modulated filter bank based on the proposed efficient algorithms for calculating distortion and spectral aliasing in the filter bank.

Electromagnetic field and artificial intelligence based fault detection and classification system for the transmission lines in smart grid

ABSTRACT An electromagnetic field and artificial intelligence-based transmission line protection system for smart grid is proposed in this paper. Bayesian regularization neural network is used as an intelligence technique. Instead of conventional current transformers, the use of magnetic field sensors for monitoring the current in transmission lines is proposed. A mathematical model of the magnetic field due to the current in transmission line is developed by considering two positions of the magnetic field sensors. The developed model is simulated for extensive scenarios. Line to ground, line to line, double line to ground and three-phase faults are created. The fault current values are transformed into magnetic field values and given to the dyadic analysis filter bank for feature extraction process. The extracted features are given as an input to the neural network for the training. The Bayesian regularization algorithm is used as a training algorithm. The performance of the proposed system is compared with other algorithms. It shows that the proposed system with Bayesian regularization have 96.11% of accuracy with the combination of different faults. The results obtained show that the proposed system outperforms the existing approaches.

NANO-studio, the design environment of filter banks implemented in standard CMOS technology

The paper presents a method of optimizing frequency characteristics of filter banks in terms of their implementation in digital CMOS technologies in nanoscale. Usability of such filters is demonstrated by frequency-interleaved (FI) analog-to-digital converters (ADC). An analysis filter present in these converters was designed in switched-current technique. However, due to huge technological pitch of standard digital CMOS process in nanoscale, its characteristics substantially deviate from the required ones. NANO-studio environment presented in the paper allows adjustment, with transistor channel sizes as optimization parameters. The same environment is used at designing a digital synthesis filter, whereas optimization parameters are input and output conductances, gyration transconductances and capacitances of a prototype circuit. Transition between analog s and digital z domains is done by means of bilinear transformation. Assuming a lossless gyrator-capacitor (gC) multiport network as a prototype circuit, both for analysis and synthesis filter banks in FI ADC, is an implementation of the strategy to design filters with low sensitivity to parameter changes. An additional advantage is designing the synthesis filter as stable infinite impulse response (IIR) instead of commonly used finite impulse response (FIR) filters. It provides several dozen-fold saving in the number of applied multipliers.. The analysis and synthesis filters in FI ADC are implemented as filter pairs. An additional example of three-filter bank demonstrates versatility of NANO-studio software.

Jarvis: Desktop Assistant

“Jarvis” was main character of Tony’s Stark’s life assistant in Movies Iron Man. Unlike original comic in which Jarvis was Stark’s human butler, the movie version of Jarvis is an intelligent computer that converses with stark, monitors his household and help to build and program his superhero suit. In this Project Jarvis is Digital Life Assistant which uses mainly human communication means such Twitter, instant message and voice to create two way connections between human and his apartment, controlling lights and appliances, assist in cooking, notify him of breaking news, Facebook’s Notifications and many more. In our project we mainly use voice as communication means so the Jarvis is basically the Speech recognition application. The concept of speech technology really encompasses two technologies: Synthesizer and recognizer. A speech synthesizer takes as input and produces an audio stream as output. A speech recognizer on the other hand does opposite. It takes an audio stream as input and thus turns it into text transcription. The voice is a signal of infinite information. A direct analysisand synthesizing the complex voice signal is due to too much information contained in the signal. Therefore the digital signal processes such as Feature Extraction and Feature Matching are introduced to represent the voice signal. In this project we directly use speech engine which use Feature extraction technique as Mel scaled frequency cepstral. The mel- scaled frequency cepstral coefficients (MFCCs) derived from Fourier transform and filter bank analysis are perhaps the most widely used front- ends in state-of-the-art speech recognition systems. Our aim to create more and more functionalities which can help human to assist in their daily life and also reduces their efforts. In our test we check all this functionality is working properly. We test this on 2 speakers(1 Female and 1 Male) for accuracy purpose.

Prediction of time series using an analysis filter bank of LSTM units

Time series emerge in various applications such as financial data and production data, however, most of the generated data exhibit nonlinear inter-dependency between samples and noise, making necessary the development of methods capable of handling such nonlinearities and other abnormalities. In this paper we present an architecture for prediction of time series embedded in noise. The proposed architecture combines a convolutional and long short term memory (LSTM) layers into a structure similar to an analysis filterbank of two channels. The first element of each channel is a convolutional layer followed by a LSTM, which is able to find temporal dependencies of the signal. Finally the channels are summed to obtain a prediction. We found that the frequency response of the filters resemble a complementary filter bank response, with each channel having a maximum at different bands which could suggest that it characterizes the incoming signal in frequency. Comparisons with other methods demonstrate that the proposed method offer much better results in terms of different error measures.

A New Subject-Specific Discriminative and Multi-Scale Filter Bank Tangent Space Mapping Method for Recognition of Multiclass Motor Imagery.

Objective: Tangent Space Mapping (TSM) using the geometric structure of the covariance matrices is an effective method to recognize multiclass motor imagery (MI). Compared with the traditional CSP method, the Riemann geometric method based on TSM takes into account the nonlinear information contained in the covariance matrix, and can extract more abundant and effective features. Moreover, the method is an unsupervised operation, which can reduce the time of feature extraction. However, EEG features induced by MI mental activities of different subjects are not the same, so selection of subject-specific discriminative EEG frequency components play a vital role in the recognition of multiclass MI. In order to solve the problem, a discriminative and multi-scale filter bank tangent space mapping (DMFBTSM) algorithm is proposed in this article to design the subject-specific Filter Bank (FB) so as to effectively recognize multiclass MI tasks.Methods: On the 4-class BCI competition IV-2a dataset, first, a non-parametric method of multivariate analysis of variance (MANOVA) based on the sum of squared distances is used to select discriminative frequency bands for a subject; next, a multi-scale FB is generated according to the range of these frequency bands, and then decompose multi-channel EEG of the subject into multiple sub-bands combined with several time windows. Then TSM algorithm is used to estimate Riemannian tangent space features in each sub-band and finally a liner Support Vector Machines (SVM) is used for classification.Main Results: The analysis results show that the proposed discriminative FB enhances the multi-scale TSM algorithm, improves the classification accuracy and reduces the execution time during training and testing. On the 4-class BCI competition IV-2a dataset, the average session to session classification accuracy of nine subjects reached 77.33 ± 12.3%. When the training time and the test time are similar, the average classification accuracy is 2.56% higher than the latest TSM method based on multi-scale filter bank analysis technology. When the classification accuracy is similar, the training speed is increased by more than three times, and the test speed is increased two times more. Compared with Supervised Fisher Geodesic Minimum Distance to the Mean (Supervised FGMDRM), another new variant based on Riemann geometry classifier, the average accuracy is 3.36% higher, we also compared with the latest Deep Learning method, and the average accuracy of 10-fold cross validation improved by 2.58%.Conclusion: Research shows that the proposed DMFBTSM algorithm can improve the classification accuracy of MI tasks.Significance: Compared with the MFBTSM algorithm, the algorithm proposed in this article is expected to select frequency bands with good separability for specific subject to improve the classification accuracy of multiclass MI tasks and reduce the feature dimension to reduce training time and testing time.

Joint Channel Estimation and Equalization Using New AFB Output Signal Models for FBMC/OQAM Systems

Filter bank multicarrier with offset quadrature amplitude modulation (FBMC/OQAM) has been studied as an alternative waveform for post-orthogonal frequency division multiplexing (OFDM) wireless communications. However, due to the intrinsic imaginary inter-carrier/inter-symbol interference of FBMC/OQAM signals, accurate channel estimation and equalization remain open issues for FBMC/OQAM systems in dispersive channels. In this article, two new signal models of the analysis filter bank (AFB) output are first derived in the absence of any channel assumption and in the presence of a relaxed channel assumption, respectively. Based on the new-built models, initial channel estimation with least squares (LS) (IniCE-LS) is presented and its output is used as the initial condition of recursive weighted LS (RWLS) estimation; moreover, non-iterative and iterative channel equalization combined with RWLS channel estimation (i.e., N-ICE-RWLS and ICE-RWLS) are proposed by using the real-time reconstructed FBMC/OQAM data symbols as pilots. The proposed N-ICE-RWLS and ICE-RWLS schemes not only allow to improve channel estimation and equalization performance, but also achieve high transmission efficiency. Considering the requirements of practical application, we further develop the low-complexity implementation methods of N-ICE-RWLS and ICE-RWLS. All these schemes are validated by analyzing the channel estimation accuracy and bit error rate (BER) over various frequency selective channels.

Novel filter bank-based cooperative spectrum sensing under practical challenges for beyond 5G cognitive radios

Cognitive radio (CR) technology with dynamic spectrum management capabilities is widely advocated for utilizing effectively the unused spectrum resources. The main idea behind CR technology is to trigger secondary communications to utilize the unused spectral resources. However, CR technology heavily relies on spectrum sensing techniques which are applied to estimate the presence of primary user (PU) signals. This paper firstly focuses on novel analysis filter bank (AFB) and FFT-based cooperative spectrum sensing (CSS) techniques as conceptually and computationally simplified CSS methods based on subband energies to detect the spectral holes in the interesting part of the radio spectrum. To counteract the practical wireless channel effects, collaborative subband-based approaches of PU signal sensing are studied. CSS has the capability to relax the problems of both hidden nodes and fading multipath channels. FFT- and AFB-based receiver side sensing methods are applied for OFDM waveform and filter bank-based multicarrier (FBMC) waveform, respectively, the latter one as a candidate beyond-OFDM/beyond-5G scheme. Subband energies are then applied for enhanced energy detection (ED)-based CSS methods that are proposed in the context of wideband, multimode sensing. Our first case study focuses on sensing potential spectral gaps close to relatively strong primary users, considering also the effects of spectral regrowth due to power amplifier nonlinearities. The study shows that AFB-based CSS with FBMC waveform is able to improve the performance significantly. Our second case study considers a novel maximum–minimum energy detector (Max–Min ED)-based CSS. The proposed method is expected to effectively overcome the issue of noise uncertainty (NU) with remarkably lower implementation complexity compared to the existing methods. The developed algorithm with reduced complexity, enhanced detection performance, and improved reliability is presented as an attractive solution to counteract the practical wireless channel effects under low SNR. Closed-form analytic expressions are derived for the threshold and false alarm and detection probabilities considering frequency selective scenarios under NU. The validity of the novel expressions is justified through comparisons with respective results from computer simulations.

A new robust delayless subband adaptive filtering algorithm with variable step sizes for active control of broadband noise

Conventional delayless subband active noise control (ANC) systems can be degraded by the unexpected impulsive disturbances in the reference signal and/or the residual error signals despite the fact that the step sizes have been normalized by the variances of subband signals. Another inherent drawback is that an undesirable delay is introduced into the weight update path by using analysis filter banks, which reduces the upper bound of the step sizes thus limits the convergence rate. This paper proposes a novel robust and effective ANC algorithm configured in the delayless subband architecture for broadband noise cancellation. Instead of real-time providing threshold(s) on the reference and/or error signal as applied in most of the existing ANC algorithms for robustness, an online tuning scheme of bounded step sizes for the adaptive learning is developed for better convergence and outlier suppression. Box-constraint and time-averaging scheme deployed in the step size tuning guarantee robustness without requiring very accurate a priori information of the noises which might be corrupted by the impulses in real-world applications. In particular, this paper presents the stability and convergence analysis of the proposed closed-loop ANC system. Moreover, the computational complexity advantage is justified when compared to other state-of-the-art robust ANC algorithms. Numerical simulations are performed to validate the enhanced performance of the proposed algorithm for various colored noises with or without impulsive interferences.