Signal Bandwidth Research Articles

On the Arithmetic Complexity of the Bandwidth of Bandlimited Signals

The bandwidth of a signal is an important physical property that is of relevance in many signal- and information-theoretic applications. In this paper we study questions related to the computability of the bandwidth of computable bandlimited signals. To this end we employ the concept of Turing computability, which exactly describes what is theoretically feasible and can be computed on a digital computer. Recently, it has been shown that there exist computable bandlimited signals with finite energy, the actual bandwidth of which is not a computable number, and hence cannot be computed on a digital computer. In this work, we consider the most general class of band-limited signals, together with different computable descriptions thereof. Among other things, our analysis includes a characterization of the arithmetic complexity of the bandwidth of such signals and yields a negative answer to the question of whether it is at least possible to compute non-trivial upper or lower bounds for the bandwidth of a bandlimited signal. Furthermore, we relate the problem of bandwidth computation to the theory of oracle machines. In particular, we consider halting and totality oracles, which belong to the most frequently investigated oracle machines in the theory of computation.

Deeply-Coupled GNSS Dual-Band Collaborative Tracking Using the Extended Strobe Correlator

Continuous and precise code delay tracking is essential in challenging environments such as urban canyons where multipath and frequent signal blockage may lead to performance degradation and loss of lock of signals. The strobe correlator (SC) is an effective technique for baseband multipath mitigation and is commonly used for GNSS signal tracking enhancement. An SC has two pairs of early and late correlators, one with a wide and another with a narrow correlator spacing. This paper extends the conventional SC design by applying its correlator pairs on dual-band GNSS signals to realize an extended strobe correlator (ESC). An ESC takes advantage of the natural distinct bandwidth of narrow-band and wide-band signals, e.g., L1 C/A and L5Q signals, to form a dual-band combination at the correlator level in a deeply-coupled manner. Narrow-band L1 signal tracking ensures robustness, while the wide-band L5 signal improves the tracking precision. A moving average window filters the L1 measurement to prevent the noise impact on L5 tracking, providing necessary inter-band assistance to resolve challenges such as code delay misalignment and precision mismatch. Through both Monte Carlo simulation and real data collected in urban environments, this work demonstrates that an ESC can achieve improved overall robustness against loss-of-lock and multipath mitigation performance over standalone tracking. Compared with KF-based combined (KFC-based) tracking, ESC has higher tracking precision under weak signal environment and robustness during signal blockage, but larger residual multipath error.

Estimation of the Immunity of a Satellite Communication System Based on Wideband Signals in Relation to Impulse and Fluctuating Interferences

A model of a satellite communication system based on wideband signals with relative diphase encoding is developed in the paper. A system of four signals with binary phase shift keying is used. Spectra of the signals are spread by the direct sequence technique. The developed system operation under conditions of impulse electromagnetic interference and under the influence of fluctuation interference is simulated. The dependences of bit error rate on signal-to-interference ratio are evaluated in the cases of impulse interference with Johnson distribution and white Gaussian noise affecting the signal.

A System Optimization Scheme for Bias Correction of Polarimetric Phased-Array Radar

With the change in the spatial angle, the cross-polarization isolation (CPI) of polarimetric phased-array radar (PPAR) changes as well, destroying the estimation of the target polarization scattering matrix (PSM). To correct the bias in PPAR, this article comprehensively designs the transmitting antenna, receiving antenna, and signal waveform and proposes a bias correction method based on system optimization. First, for the transmitting antenna of PPAR, the second-order cone program (SOCP) model is proposed to optimize the weighting coefficient. With the SOCP-based beamforming method, not only beam pattern in any spatial angle can be achieved, but also arbitrary polarization state can be precisely configured. Then, for the wideband receiving signal with a certain beamwidth, an angle estimation method based on eigenvalue decomposition is proposed in this article, which can effectively cure the challenges introduced by the beamwidth and signal bandwidth. Subsequently, for the signal waveform, the phase code is designed to measure all the elements of PSM in simultaneous transmission and simultaneous reception (STSR) mode, which could eliminate the biases of the moving speed and the second-order cross-polarization error. Finally, this article compares with other methods based on differential reflectivity, and experiments show that the factors such as the spatial angle, array structure, antenna beamwidth, signal bandwidth, motion speed, and signal-to-noise ratio (SNR) have the least influence on the present method in this article.

Bandwidth-Scalable Digital Predistortion of Active Phased Array Using Transfer Learning Neural Network

This paper proposes a transfer learning neural network (TLNN) approach for digital predistortion (DPD) of mm-Wave active phased arrays (APA) operated under variable signal bandwidth regimes. Compared with the conventional artificial neural network (ANN) method, the proposed approach can achieve similar linearization performance with much lower computational complexity by transferring part of a trained model from one bandwidth to another bandwidth. In the recently introduced 5G, the increased signal bandwidth triggers considerable memory effects in the APA. Moreover, dealing with different signal bandwidths typically requires a time-consuming recalculation of the predistorter parameters. In this paper, the authors propose to have those challenges solved by using a DPD model based on the transfer learning method. The proposed approach was validated with over-the-air (OTA) measurements on an APA excited with signals of varying bandwidth, namely from 20 MHz to 100 MHz. Experimental results show a significant reduction in the training time while ensuring good linearization performance. With the applied TLNN DPD, an 8.5 dB improvement of adjacent channel leakage ratio (ACLR) and 8.6 % points improvement of error vector magnitude (EVM) is achieved. Under the variable bandwidth regime, the complexity of the DPD model in terms of the number of multiplications is reduced from 199168 to 160. The proposed TLNN DPD proved to be robust concerning variation in the bandwidth of the APA excitation signal.

A novel measure of cardiopulmonary coupling during sleep based on the synchrosqueezing transform algorithm.

This paper presents a novel method to quantify cardiopulmonary dynamics for automatic sleep apnea detection by integrating the synchrosqueezing transform (SST) algorithm with the standard cardiopulmonary coupling (CPC) method. Simulated data were designed to validate the reliability of the proposed method, with varying levels of signal bandwidth and noise contamination. Real data were collected from the Physionet sleep apnea database, consisting of 70 single-lead ECGs with expert-labeled apnea annotations on a minute-by-minute basis. Three different signal processing techniques applied to sinus interbeat interval and respiratory time series include short-time Fourier transform, continuous Wavelet transform, and synchrosqueezing transform, respectively. Subsequently, the CPC index was computed to construct sleep spectrograms. Features derived from such spectrogram were used as input to five machine- learning-based classifiers including decision trees, support vector machines, k-nearest neighbors, etc. Results: The simulation results showed that the SST-CPC method is robust to both noise level and signal bandwidth, outperforming Fourier-based and Wavelet-based approaches. Meanwhile, the SST-CPC spectrogram exhibited relatively explicit temporal-frequency biomarkers compared with the rest. Furthermore, by integrating SST-CPC features with common-used heart rate and respiratory features, accuracies for per-minute apnea detection improved from 72% to 83%, validating the added value of CPC biomarkers in sleep apnea detection. The SST-CPC method improves the accuracy of automatic sleep apnea detection and presents comparable performances with those automated algorithms reported in the literature. The proposed SST-CPC method enhances sleep diagnostic capabilities, and may serve as a complementary tool to the routine diagnosis of sleep respiratory events.

Digital Linearization of Wideband Envelope Tracking Power Amplifiers for Mobile Terminals

This article presents a low-complexity linearization method to compensate for multisource nonlinear distortion in wideband envelope tracking (ET) power amplifiers (PAs) for 5G new radio (NR) mobile terminals. The proposed linearization approach consists in an envelope leakage cancellation (ELC) system operating in the dynamic supply path and a 2-D digital predistortion (2-D-DPD) linearizer acting on the baseband complex modulated signal. In a first step, an envelope generalized memory polynomial (EGMP) behavioral model is proposed to compensate for the unwanted radio frequency (RF) leakage that appears at the output of ET modulator (ETM). Then, a baseband 2-D-DPD linearizer based on a slow envelope-dependent generalized memory polynomial (SED-GMP) behavioral model is used to further enhance the linearization performance. The proposed method is validated on a system-on-chip (SoC) ET PA board for mobile applications. The reported experimental results show how the proposed digital linearization approach can mitigate the linearity and efficiency trade-off in ET PAs. Consequently, the out-of-band linearity requirement of −36 dBc of adjacent channel power ratio (ACPR) is met for 5G-NR test signals with bandwidths ranging from 60 to 200 MHz at 2.55 GHz (band B41), with an overall ET PA power efficiency ranging from 10.1% and up to 16.5%, depending on the signal bandwidth.

A New Data Processing Method for Magnetic Anomaly Detection and Localization Based on 2-D Orthonormal Basis Functions

This work proposes a new processing method based on orthonormal basis functions (OBFs) for two-dimensional (2D) magnetic anomaly data, which is an innovative extension of one-dimensional (1D) OBFs. In most surveys, ferromagnetic targets are usually regarded as magnetic dipoles. The magnetic anomaly field of a dipole in a horizontal plane is essentially a linear combination of linearly independent basis functions, which are then transformed into a set of 2D orthonormal basis functions (2D-OBFs) using the Gram–Schmidt algorithm. On the basis of the 2D-OBFs, a processing procedure for 2D magnetic anomaly data is established and named the 2D-OBF method. This method converts magnetic anomaly maps to energy maps, in which there is only one positive peak corresponding to the horizontal location of a magnetic dipole. As a consequence, the detection and localization are significantly simplified. To verify this new method, a total of 3000 Monte Carlo simulations were carried out, and the synthetic data were also processed using the traditional 1D-OBF method. The statistics of the 2D-OBF results showed that the localization accuracy and false alarm rate are 0.34 m and 1.9 %, respectively, at a data noise level of 10 nT, which was much better than the 1D-OBF method. Another synthetic data experiment verified the reliability of the 2D method for handling multiple targets simultaneously, even when the magnetic anomalies of adjacent targets are severely superimposed. For the measured aeromagnetic data, the 2D-OBF method also works well. Additionally, this method exhibits outstanding noise immunity in all tests. The signal-to-noise ratios (SNR) of the energy data are improved by approximately 14 dB compared to that of the magnetic anomaly data within the signal bandwidth. Developed from rigorous mathematical physics definitions, the 2D-OBF method is more conducive to the implementation of automatic data processing programs and has considerable practical promise.

Weighted Frequency Smoothing for Enhanced Speaker Localization

The coherent signal subspace method may be used in order to apply subspace localization methods (e.g. MUSIC) to coherent sources. This method involves a focusing process followed by frequency smoothing, which is intended to decorrelate source signals from coherent sources. In practice, however, only moderate decorrelation is obtained, which may lead to performance degradation. Although decorrelation can be improved by widening the smoothing bandwidth, a wider bandwidth may increase focusing error and the smoothing bandwidth is limited by the bandwidth of the actual signal. In this paper, a weighted frequency smoothing that improves decorrelation for a given bandwidth is proposed. It is shown that better decorrelation is obtained by selecting the weights to be inversely proportional to the source signal power at the given frequency. However, since the power of the source is not known, it is estimated by the trace of the array spatial covariance matrix. An experimental study is presented that investigates the effect of the proposed weighting on DOA estimation of speech sources in a reverberant environment.

Study on weak signal enhancement method in wireless communication under electromagnetic interference environment

In order to overcome the problems of high bit error rate and high signal enhancement time in weak signal enhancement methods, this paper proposes a weak signal enhancement method in wireless communication under electromagnetic interference environment. Firstly, the transmission characteristics of electromagnetic wave are analysed and the attenuation results of electromagnetic wave signal transmission are determined. Then, the bandwidth of weak signal is determined by band-pass sampling theorem, and the key characteristics of weak signal are determined by time-domain characteristic analysis. Finally, the amplitude modulation coefficient is used to adjust the frequency offset and initial phase through a low-pass filter to enhance the weak signal in wireless communication. The experimental results show that the lowest bit error rate of this method is about 1%, the weak signal enhancement time is no more than 2.6 s, and the weak signal enhancement effect is good.

Exploiting On-Demand 5G Downlink Signals for Opportunistic Navigation

This letter presents the first user equipment (UE)-based 5G navigation framework that exploits the “on-demand” 5G downlink signals. In this framework, the entire system bandwidth of incoming 5G signals is utilized in an opportunistic fashion. The proposed framework involves a cognitive approach to acquire the so-called ultimate reference signal (URS), which includes the “on-demand” as well as “always-on” reference signals (RSs). Experimental results are presented showing that the acquired URS: (i) spans the entire 5G downlink bandwidth, (ii) increases the carrier-to-noise ratio by 10 dB compared to state-of-the-art 5G user equipment (UE)-based opportunistic navigation receiver, and (iii) reduces significantly the carrier and code phase errors. A ranging error standard deviation of 2.75 m was achieved with proposed framework with a stationary receiver placed 290 m away from a 5G gNB in a clear line-of-sight environment, which is lower than the 5.05 m achieved when using the “always-on” 5G downlink signals.

Application of Compressive Sensing (CS) to Wide-Band Cognitive Radio signals

Compressive Sensing (CS) is a digital signal processing developed theory that encloses the signal sampling and compression, based on the sparsity characteristics of signal. This can decrease sampling rate, so reduce computational complexity of the system without degrading the performance of the system. This paper describes the theoretical frame and a few key technical, then illustrates the application of compressed sensing theory to wide-band cognitive radio signals. Spectrum sensing is a critical issue in wide-band Cognitive Radio (CR) networks as it faces hard challenges such as high hardware cost, complexity, sampling rate and processing speed. Thus, this paper shows that Compressive Sensing could be exploited in wide-band Cognitive Radio networks to solve the spectrum sensing problems mentioned above.

Design Scheme of Proximal Adaptive Noise Cancellation in Shallow Hydrological Monitoring Using a Multichannel Nuclear Magnetic Resonance Logging Tool

Small diameter nuclear magnetic resonance (NMR) logging tools are used to detect hydrologic characteristics, soil moisture, and migration of petroleum hydrocarbon contaminants near-surface in the existing hydrologic monitoring wells. However, the majority of hydrologic monitoring wells are located in densely populated areas with high electromagnetic noise levels, seriously affecting the monitoring accuracy. Consequently, decreasing electromagnetic noise is a critical component in expanding the use of small-diameter NMR logging tools. In this study, a proximal adaptive noise cancellation (PANC) method was introduced for a small-diameter NMR tool. A simulation model is used to evaluate the performance of various ANC algorithms in different conditions. The effect of coil size and position on the PANC method was then analyzed. Subsequently, a static experiment is conducted to demonstrate the validity of the method. The results show that the PANC method achieves 55% noise energy suppression without affecting the target signal intensity and bandwidth.

A Frequency-Domain I/Q Imbalance Calibration Algorithm for Wideband Direct Conversion Receivers Using Low-Cost Compensator

The amplitude and phase imbalances of the in-phase (I) and quadrature (Q) branches inherent in the direct conversion receiver structure cause the generation of image frequency interference signals. In this paper, a frequency-domain I/Q imbalance calibration algorithm is proposed for wideband direct conversion receivers. The I/Q imbalance model is rebuilt by applying the infinitesimal method and FFT algorithm. The mathematical expressions for the exact computation of the I/Q imbalance parameters are derived based on the frequency-domain statistical properties of the baseband signal. The two phase parameters, frequency-dependent I/Q imbalance (FD-IQI) and frequency-independent I/Q imbalance (FI-IQI), are separated according to the parity properties of the imbalance parameter. To avoid the interference from the transmitter, the receiver impairment estimation is performed using a frequency offset (FO) DC training signal, and a low-cost real-valued compensation (RVC) filter is introduced to correct the impairments of the received signal. The performances of the proposed calibration model are evaluated through simulations and experiments. The simulation results show that the image rejection ratio (IRR) is improved to 80-120 dBc and can also exceed 40 dBc at high noise levels. The experimental results based on the CX9261A evaluation board show that the average IRR of the multi-tone signal is increased by 24.99 dB, and the IRR of the wideband signal is increased by 19.08 dB.

An End-to-End Deep Learning Framework for Wideband Signal Recognition

Successful management of the radio spectrum requires, as a first step, detailed information about spectrum occupancy. In this work, we present an end-to-end deep learning (DL) based framework to obtain information from wide spectrum bands through signal detection, localization, and modulation classification. By visually representing the radio signals in spectrograms, we formulate the wideband detection problem as an object detection task from the computer vision field. To this end, the proposed framework consists of two cascaded modules: an object detection network repurposed to detect and classify distinctive signals in wideband spectrograms, and a convolutional neural network (CNN) designed to extend the classification capabilities to support a wide range of analog and digital modulation schemes. To evaluate our framework, we use a public wideband recognition dataset, which we carefully analyze and curate through a series of preprocessing techniques. To tackle the challenges of insufficient training data and class imbalance observed in the dataset, we suggest a training strategy that includes data mixing and transfer learning. Our experimental results on a general test set demonstrate that the proposed approach can detect and classify a variety of narrowband signals with simultaneously high precision (77.1%), recall (81.8%), and localization accuracy, as indicated by an average Intersection over Union (IoU) of 86%.

Waveform Design and DoA-DoD Estimation of OFDM-LFM Signal Based on SDFnT for MIMO Radar

In comparison to orthogonal frequency division multiplexing (OFDM), the OFDM linear frequency modulation (OFDM-LFM) signal has similar time-bandwidth product, lower peak-to-average power ratio, and higher sensitivity to Doppler frequency shift, making it a promising candidate waveform for multiple-in multiple-out (MIMO) radar applications. However, the OFDM-LFM signal is unsatisfactory in practical applications due to the challenges in waveform design and its non-stationary property, which significantly degrades the performance of current high-resolution subspace techniques for direction of departure (DoD) and direction of arrival (DoA) angle estimation. In this paper, we first proposed a novel OFDM-LFM waveform design method based on the scale discrete Fresnel transform (SDFnT), which efficiently generates orthogonal LFM signals with unlimited number of subcarriers and can be convieniently implemented using the fast Fourier transform (FFT). Then, for angle estimation in OFDM-LFM MIMO radars, we presented a new scale discrete Fresnel transform multiple signal classification (SDFnT-MUSIC) method. By converting the steering vector into the chirp domain and making it non-time-vary, high resolution angle estimation for OFDM-LFM wideband signals can be achieved. Simulation results shows that the proposed waveform design method requires less computational time than conventional method based based on the fractional Fourier transform (FrFT), whereas the proposed DoA-DoD estimation method outperform the FrFT-MUSIC method in terms of computational complexity and target angle estimate performance.

A MATLAB Simulink Study on the Performance of QAM Modulation Scheme in AWGN, Rayleigh, and Rician Fading Channels: BER Analysis

Wireless communication is the fastest-growing segment in the communication industry, with mobile communication being the most widely used. However, it faces several technical challenges, such as Fading, Shadowing, Interference, and Propagation path loss. Meeting the higher demand for capacity with high-quality service is crucial. Orthogonal Frequency Division Multiplexing (OFDM) is a technique that converts wideband signals into narrowband signals for transmission, making it a suitable option for high bandwidth data transmission. The transmission of these narrowband signals is executed with an orthogonal carrier. This paper focuses on building a QAM model using MATLAB to simulate Bit Error Rate (BER) performance for real data communication under different communication channels, including AWGN and fading channels (Rayleigh and Rician). The aim is to investigate the reduction of noise and bit error rate in communication channels. The simulation model built for this research work demonstrates that QAM scheme performs better in AWGN channels than Rayleigh or Rician fading channels.

Wideband Noncontact Voltage Measurement for EMC Applications: Design and Implementation

A methodology of amplitude and phase compensation for non-contact voltage measurements, which can be applied to wideband signal and noise observation in the EMC application, is presented. The non-contact techniques are based on a small amount of coupling capacitance between probe electrode and cable conductor where the observed signal is induced. In this case, a main technical issue to be overcome is the amplitude and phase error introduced by stray capacitance of the internal circuit, which needs to be resolved for application of the wideband EMC measurements. Herein, a design and implementation of phase compensation with inverting OpA configuration are discussed. Moreover, by introducing a system calibration with numerical process, validity for recovering the frequency response at 100 Hz ~ 100 MHz is indicated. In the calibration process, frequency compensation for dielectric dispersion of the PVC cable is also discussed. As for the time-domain measurement, the evaluation result of the fast transient impulsive noise of tr/tf: 10 ns emitted from a commercial power supply module is also discussed.

Explicit Performance Limit for Joint Range and Direction of Arrival Estimation in Phased-Array Radar Sensors

For radar sensors in vehicle safety systems, it is of importance to establish a fundamental performance limit for the estimation accuracy of targets. Bayesian bounds, such as the ZivZakai bound (ZZB), can provide an accurate prediction for the performance limit under all signal-to-noise ratio (SNR) conditions. However, Bayesian bounds for the joint estimation of multiple parameters in radar systems have not been sufficiently studied and the explicit expression is still unavailable. In this paper, we employ the thought of Shannon's information theory to propose a tight bound with closed-form expression for joint range and direction of arrival (DOA) estimation in phased-array radar. Based on the joint <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">a posteriori</i> probability density function, we define range-DOA entropy error (RDEE) as the entropy power of range and DOA. With normalized <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">a posteriori</i> entropies in low and high SNRs, the closed-form approximation for the theoretical RDEE is derived. The explicit theoretical RDEE is comprehensive and captures the effect of the SNR, the number of elements, and the ratio of the signal bandwidth to the carrier frequency. The explicit theoretical RDEE is compared with the Cramér-Rao bound and the ZZB by a numerical simulation and the result shows that the theoretical RDEE can provide a more accurate performance prediction for the joint range-DOA estimation in phased-array radar.

A Power/Hardware-Efficient SiPM Readout IC Embedded in a Boost Converter for Mobile Radiation Dosimeters

A radiation detection system provides radiation intensity to protect human life from hazardous radiation. However, the prior portable radiation detection system (mobile radiation dosimeter) structures had a problem in that various subsystems had to be designed with complex circuits. First, since the radiation detector must be driven by a relatively high voltage, a high-efficient boost converter that can receive the battery voltage and generate a high bias voltage must be implemented. Second, sophisticated sensor interface circuits that can convert the analog signal generated by the radiation detector to digital value must also be implemented. To reduce the complexity of the mobile radiation dosimeters, a power- and hardware-efficient radiation detection system IC is presented for mobile radiation dosimeters. The current value of a silicon photomultiplier (SiPM) used as a radiation detector is sensed from the control information of a boost converter in the process of regulating a voltage to bias the SiPM. Due to this embedded sensing function, no additional hardware and power consumption are required for implementing and operating separate sensor interface circuits while providing sufficient radiation sensing performance. The implemented radiation detection system IC achieves 0.217- <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">μ</i> A <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">rms</sub> input referred noise performance over 1-kHz signal bandwidth and 10-mA maximum allowable linear input current range. In addition, the proposed dc–dc boost converter can generate a high enough voltage (∼27 V) to drive SiPM from a lithium-ion battery with an efficiency of 72%. The irradiation test with radiation check source ( <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">137</sup> Cs) demonstrates that the bias voltage is well regulated in the presence of high-energy radiation particles.