Adaptive Bandwidth Research Articles

Maximizing transmission capacity in optical communication systems utilizing a microresonator comb laser source with adaptive modulation and bandwidth allocation strategies

Traditional optical communication systems employ bulky laser arrays that lack coherence and are prone to severe frequency drift. Dissipative Kerr soliton microcombs offer numerous evenly spaced optical carriers with a high optical signal-to-noise ratio (OSNR) and coherence in chip-scale packages, potentially addressing the limitations of traditional wavelength division multiplexing (WDM) sources. However, soliton microcombs exhibit inhomogeneous OSNR and linewidth distributions across the spectra, leading to variable communication performance under uniform modulation schemes. Here, we demonstrate, for the first time, to our knowledge, the application of adaptive modulation and bandwidth allocation strategies in optical frequency comb (OFC) communication systems to optimize modulation schemes based on OSNR, linewidth, and channel bandwidth, thereby maximizing capacity. Experimental verification demonstrates that the method enhances spectral efficiency from 1.6 to 2.31 bit ⋅ s−1 ⋅ Hz−1, signifying a 44.58% augmentation. Using a single-soliton microcomb as the light source, we achieve a maximum communication capacity of 10.68 Tbps after 40 km of transmission in the C-band, with the maximum single-channel capacity reaching 432 Gbps. The projected combined transmission capacity for the C- and L-bands could surpass 20 Tbps. The proposed strategies demonstrate promising potential of utilizing soliton microcombs as future light sources in next-generation optical communication.

Confidence-based design optimization using multivariate kernel density estimation under insufficient input data

The uncertainty quantification of the input statistical model in reliability-based design optimization (RBDO) has been widely investigated for accurate reliability analysis, and it could be estimated through its characteristics, cumulative experiences, and available data. However, uncertainty quantification of random variables in existing RBDO studies has exploited parametric distributions quantifying the uncertainty through the Bayes' theorem. In addition, a correlation between random variables is often underestimated due to a lack of knowledge and difficulty to describe the high-dimensional correlation. Hence, it has been a challenge to properly quantify input statistical model and its uncertainty. Therefore, a multivariate kernel density estimation (KDE) is employed to perform data-driven confidence-based design optimization (CBDO) for effective quantification of input model uncertainty. Any assumption on input distribution is not necessary since it is established only with the given input data. Moreover, the input model uncertainty due to insufficient data is quantified using bootstrapping and optimal adaptive bandwidth matrices through the Bayes’ theorem using cross-validation error. Consequently, the proposed CBDO with given input data is capable of finding a conservative optimum of RBDO accounting for both aleatory uncertainty of random variables and epistemic uncertainty induced by a limited number of input data through the multivariate KDE.

Joint probabilistic modeling approach for harmonic and three-phase unbalanced disturbance sources

Harmonics and three-phase imbalance are two common disturbances in power systems that interact with and affect each other, leading to more complex impacts on power system performance. To comprehensively and accurately analyze the distribution and interaction mechanisms of harmonics and three-phase imbalance, this study investigated the joint emission level of harmonic and three-phase unbalanced disturbance sources (H-TU-DS). This paper proposes a joint probabilistic emission level modeling approach. The power, harmonic, and three-phase unbalanced data of H-TU-DS are multidimensional, and the feature dataset is filtered by calculating multiple correlation coefficients to achieve dimensionality reduction without losing data information. Considering the different operating characteristics of the H-TU-DS, an improved fuzzy C-means (FCM) algorithm is applied to classify operating conditions, enabling a comprehensive and accurate analysis of the joint emission level. Based on the nonparametric kernel density function, joint probability modeling of H-TU-DS is performed using an adaptive bandwidth improvement method, effectively enhancing the accuracy of the modeling process. Finally, the effectiveness and accuracy of the proposed methods for feature dataset filtering, operating condition classification, and joint probability modeling are demonstrated through comparisons with both simulated and actual data. The proposed model facilitates the prediction and assessment of the impact of H-TU-DS on power quality after grid connection, enabling the effective prevention of power-quality problems through advanced treatment.

Multi-Path Data Transmission System Based on 5G Communication Technology

Due to the current problems of 5G(5th Generation Mobile Communication Technology) networks such as discontinuous coverage, poor coverage performance, and insufficient uplink rate, the real-time transmission performance of 5G networks has been greatly affected, and cannot meet the growing needs of high-speed communication and applications based on the Internet of Things. This paper aims to improve the transmission effect of 5G communication technology by improving the algorithm. By analyzing the problems of existing 5G bandwidth estimation algorithm, this paper proposes a Westwood-based coupling adaptive bandwidth estimation algorithm, and describes the algorithm ideas involved in the algorithm point-by-point. Moreover, this paper uses adaptive bandwidth estimation algorithm to update the slow start threshold in the fast retransmission phase, which makes the congestion window size of path transmission more reasonable. Simulation results show that the congestion control algorithm proposed in this paper improves the accuracy of link bandwidth estimation to a certain extent, improves the link congestion situation and improves the throughput of multipath transmission links. This article can be applied to the algorithm in multipath data transmission to further improve data processing efficiency and processing speed.Therefore, the improved method in this paper can be used in the practice of 5G communication to further improve the efficiency of 5G communication.

A Circle Center Location Algorithm Based on Sample Density and Adaptive Thresholding

How to acquire the exact center of a circular sample is an essential task in object recognition. Present algorithms suffer from the high time consumption and low precision. To tackle these issues, we propose a novel circle center location algorithm based on sample density and adaptive thresholding. After obtaining circular contours through image pre-processing, these contours were segmented using a grid method to obtain the required coordinates. Based on the principle of three points forming a circle, a data set containing a large number of samples with circle center coordinates was constructed. It was highly probable that these circle center samples would fall within the near neighborhood of the actual circle center coordinates. Subsequently, an adaptive bandwidth fast Gaussian kernel was introduced to address the issue of sample point weighting. The mean shift clustering algorithm was employed to compute the optimal solution for the density of candidate circle center sample data. The final optimal center location was obtained by an iteration algorithm. Experimental results demonstrate that in the presence of interference, the average positioning error of this circle center localization algorithm is 0.051 pixels. Its localization accuracy is 64.1% higher than the Hough transform and 86.4% higher than the circle fitting algorithm.

Optimized search for a binary black hole merger population in LIGO-Virgo O3 data

Maximizing the number of detections in matched filter searches for compact binary coalescence (CBC) gravitational wave signals requires a model of the source population distribution. In previous searches using the framework, sensitivity to the population of binary black hole (BBH) mergers was improved by restricting the range of filter template mass ratios and use of a simple one-dimensional population model. However, this approach does not make use of our full knowledge of the population and cannot be extended to a full parameter space search. Here, we introduce a new ranking method, based on kernel density estimation with adaptive bandwidth, to accurately model the probability distributions of binary source parameters over a template bank, both for signals and for noise events. We demonstrate this ranking method by conducting a search over LIGO-Virgo O3 data for BBHs with unrestricted mass ratio, using a signal model derived from previous significant detected events. We achieve over 10% increase in sensitive volume for a simple power-law simulated signal population, compared to the previous BBH search. Correspondingly, with the new ranking, eight additional candidate events above an inverse false alarm rate threshold 0.5 yr are identified. Published by the American Physical Society 2024

QoS Analysis of Implementation Elastic WLAN Mechanism for Adaptive Bandwidth Management Systems in Smart Buildings

QoS Analysis of Implementation Elastic WLAN Mechanism for Adaptive Bandwidth Management Systems in Smart Buildings

Adaptive composite bandwidth and automatic gain control receiver for 6G wireless optical communication

The advent of 6G communication promises a transformative leap in wireless connectivity, ushering in an era of unprecedented data rates, ultra-low latency, and pervasive connectivity. To harness the full potential of 6G networks, it is imperative to address the unique challenges posed by evolving communication environments. In this context, we present a novel framework that integrates Adaptive Composite Bandwidth and Automatic Gain Control techniques into the 6G communication paradigm. Optical wireless receivers experience large input current difference due to the large transmitted power, noise from ambient light and the varying efficiencies of different photodiode receivers. With its large dynamic range of μA to mA, transimpedance amplifiers are suitable to handle photodiode efficiency with a large dynamic range. The receiver design proposed in this article incorporates two characteristic parameter adjustments, namely, bandwidth and automatic gain. By adjusting the bandwidth the signal-to-noise ratio of the incoming signal is automatically controlled. By controlling the bandwidth, the unwanted noise is reduced and amplifier output is liable to low noise and enhances the dynamic range without extra filtering. The automatic gain control adapts its gain based on slight change in the input signal at the receiver front-end. This optimization technique ensures low photo-detection and amplification noise to achieve better quality of service. The results indicate that the bootstrap transimpedance amplifier gain is around 53.3 dB and frequency cut-off at 109.7 MHz. Thus, when gain control capacitance is varied between 50 pF to 1 nF, the bandwidth adjustment falls in the range 7.5–104.1 MHz, and the amplifier’s second stage gain becomes 10.4 dB. The overall gain of the proposed configuration with automatic gain control integrated into the transimpedance amplifier increases up to 31.1 dB, while the bandwidth increases from 9.4 to 60.7 MHz. Consequently, the gain bandwidth product is optimized from 10.4 to 31.1 dB. The main contribution of this work is optimizing the product by selecting a capacitance value within the given range that maximizes the gain-bandwidth product. This optimization paradigm is predicated on identifying a capacitance value that minimizes the gain-bandwidth product, thereby effectuating effective noise mitigation. This proposed framework embodies a significant contribution to the domain of 6G communications, heralding a new epoch in the optimization of wireless connectivity through the strategic integration of adaptive bandwidth and automatic gain control mechanism.

Design, analysis and validation of pinnacles impedance FSS absorber based on La-doped mullite ceramic

The bandwidth adaptation factor (BAF), which is calculated as BW • (λL - λH): (d • fr), is a crucial evaluation parameter for designing ultrathin broadband absorbers. In this paper, a high BAF absorber is designed by utilizing an impedance frequency selective surface (FSS), which includes a six pinnacles FSS, a La-doped mullite ceramic dielectric layer and a metal backplane. The optimal numbers of pinnacled FSS is determined through comparison and optimization, and the power monitor results indicate that the main loss modes are electrical and ohmic loss. Subsequently, the impedance analysis reveals that the favourable absorption performance is attributed to good impedance matching and high loss coefficient. Furthermore, the observation of the current distribution on the surface confirms that the primary loss appears at the pinnacles. The proposed absorber has below −10 dB reflectivity in 12.26–18.00 GHz. Meanwhile, the BAF is 3.36 and the thickness is only 0.061 λL (1.5 mm). The measured results are in good agreement with the simulation results, demonstrating an excellent BAF characteristic.

A general purpose parallel Fortran code for grid projected concentration reconstruction from multidimensional particle distributions

Particle-based transport models are widely used for simulating the movement of pollutants in environmental systems. Unlike grid-based methods, particles naturally address advective transport without numerical dispersion. However, these models require concentration reconstruction from the discrete particle information. This is computationally demanding in multidimensional problems, posing a challenge for field-scale models requiring frequent reconstruction. Grid Projected Kernel Density Estimation (GPKDE) is a cell-averaged reconstruction with improvements in computational performance compared to classical KDE. Currently, no programs implementing this method are readily integrated into particle simulators, compatible with three-dimensional domains, and particles with unequal weights. This article introduces a Fortran code for general-purpose GPKDE, with modular functionalities facilitating the integration into external software. The program implements locally adaptive kernel bandwidth selection and alternatives for the reconstruction from particles with non-uniform weights. The code is parallelized with the OpenMP library. Numerical test cases demonstrate the program’s applicability and scalability of the parallel implementation.

Method for analysing of wind power wave nature based on kernel density estimation

It is beneficial for improving the accuracy of wind power output prediction by analysing and mastering the inherent laws of the fluctuation characteristics of wind power output, guiding the power grid dispatching department to reasonably arrange power generation plans, and improving the economic efficiency of system operation. In order to characterize the probability density distribution of wind power output fluctuation, two adaptive bandwidth kernel density estimation models are established by correcting the fixed bandwidth obtained from the empirical method and unbiased cross-validation method respectively, and then the two models are combined and optimized, and ultimately, the probability density distribution model of wind power output fluctuation based on Hybrid Adaptive Kernel Density Estimation (HAKDE) is established. A variety of probability density distribution models are used to fit the wind power output fluctuations at different spatial and temporal scales in a province in North China, and the example results show that the hybrid adaptive kernel density estimation model has the best fitting effect, thus verifying the effectiveness of the hybrid adaptive kernel density estimation model.

ODRAD: An optical wireless DCN dynamic-bandwidth reconfiguration with AWGR and deep reinforcement learning

The rapid growth of Data Center Network (DCN) traffic has brought new challenges, such as limited bandwidth, high latency, and packet loss to existing DCNs based on electrical switches. Because of its theoretically unlimited bandwidth and faster data transmission speeds, optical switching can overcome the problems of electrically switched DCNs. Additionally, numerous research works have been devoted to optical wired DCNs. However, static and fixed-topology DCNs based on optical interconnects significantly limit their flexibility, scalability, and reconfigurability to provide adaptive bandwidth for traffic with heterogeneous characteristics. In this study, we propose and conduct performance evaluations on a reconfigurable optical wireless DCN architecture based on distributed Software-Defined Networking (SDN), Deep Reinforcement Learning (DRL), Semiconductor Optical Amplifier (SOA), and Arrayed Waveguide Grating Router (AWGR). Our architecture is called ODRAD (which stands for Optical Wireless DCN Dynamic-bandwidth Reconfiguration with AWGR and Deep Reinforcement Learning). A Mininet simulation model is established to further verify the reconfigurability of the ODRAD network for various server scales. Based on experimental verification, ODRAD achieves an average end-to-end server latency of 5.2μs under a load of 99%. Compression results demonstrate a 17.36% improvement in packet rate latency performance compared to RotorNet and a 15.21% improvement compared to OPSquare at a load of 99% as the ODRAD network scales from 2,560 to 40,960 servers. Furthermore, ODRAD exhibits effective throughput across different routing protocols, DCN scales and loads.

A data‐driven adaptive geospatial hotspot detection approach in smart cities

AbstractHotspot detection from geo‐referenced urban data is critical for smart city research, such as traffic management and policy making. However, the classical clustering or classification approach for hotspot detection mainly aims at identifying “hotspot areas” rather than specific points, and the setting of global parameters such as search bandwidth can lead to inaccurate results when processing multi‐density urban data. In this article, a data‐driven adaptive hotspot detection (AHD) approach based on kernel density analysis is proposed and applied to various spatial objects. The adaptive search bandwidth is automatically calculated depending on the local density. Window detection is used to extract the specific hotspots in AHD, thus realizing a small‐scale characterization of urban hotspots. Through the trajectory data of Harbin City taxis and New York City crime data, Geo‐information Tupu is used to analyze the obtained specific hotspots and verify the effectiveness of AHD, providing new ideas for further research.

Temperature Effect Separation of Structure Responses from Monitoring Data Using an Adaptive Bandwidth Filter Algorithm.

Temperature is one of the most important factors significantly affecting damage detection performance in civil engineering. A new method called the Adaptive Bandwidth Filter Algorithm (ABFA) is proposed in this paper to separate the temperature effect from quasi-static long-term structural health monitoring data. The Adaptive Bandwidth Filter Algorithm (ABFA) is referred to as an algorithm of automatically adjusting the frequency bandwidth filter via the particle swarm optimization (PSO) algorithm. Considering the obvious multi-scale feature of the collected data of civil structure, the acquired time series are divided into different time scales (for example, day, month, year, etc.), and these scales in the frequency domain correspond to the center frequencies of the adaptive bandwidth filter. The temperature effect on structure responses across different time scales is thereafter explored by adaptively adjusting the frequency bandwidth of the filter based on the known center frequencies of different scales. The relationship between the temperature and the structure responses is established through statistical regression facilitated by sufficient in situ monitoring data. Simulation and experiment results show the very promising performance of the proposed algorithm and decouple the temperature effect accurately from the contaminated data; thus an enhanced capability of damage detection is achieved.

Probabilistic Load Flow Analysis Using Nonparametric Distribution

In the pursuit of sustainable energy solutions, this research addresses the critical need for accurate probabilistic load flow (PLF) analysis in power systems. PLF analysis is an essential tool for estimating the statistical behavior of power systems under uncertainty. It plays a vital part in power system planning, operation, and dependability studies. To perform accurate PLF analysis, this article proposes a Kernel density estimation with adaptive bandwidth for probability density function (PDF) estimation of power injections from sustainable energy sources like solar and wind, reducing errors in PDF estimation. To reduce the computational burden, a Latin hypercube sampling approach was incorporated. Input random variables are modeled using kernel density estimation (KDE) in conjunction with Latin hypercube sampling (LHS) for probabilistic load flow (PLF) analysis. To test the proposed techniques, IEEE 14 and IEEE 118 bus systems are used. Two benchmark techniques, the Monte Carlo Simulation (MCS) method and Hamiltonian Monte Carlo (HMC), were set side by side for validation of results. The results illustrate that an adaptive bandwidth kernel density estimation with the Latin hypercube sampling (AKDE-LHS) method provides better performance in terms of precision and computational efficiency. The results also show that the suggested technique is more feasible in reducing errors, uncertainties, and computational time while depicting arbitrary distributions of photovoltaic and wind farms for probabilistic load flow analysis. It can be a potential solution to tackle challenges posed by sustainable energy sources in power systems.

Risk Assessment Based on KDE of Ship Collision Candidates for Ship Routing Waterway

To study the geographical distribution characteristics of maritime traffic risks, statistical representations of potential accident scenarios and macro collision risk models were established, and the waters with higher maritime traffic risks were generated. To better evaluate the risk of ship collision candidates during routing waterways, an improved adaptive bandwidth kernel density estimation (KDE) is proposed. This proposed algorithm is used for evaluating risk reduction of the ship routing system, which schedules and adjusts maritime traffic in congested harbor waterways. Larger bandwidth can make the hot spot region more obvious on a global scale. Moreover, the bandwidth is positively correlated with the dispersion of points. Concerning the data with sparse point distribution, a larger bandwidth should be used whereas, for data with dense points of interest, a smaller bandwidth should be considered. The results show that the KDE, with optimized bandwidth, can fit the ship encountering distribution and obtain the frequent spots for ship encountering. The comparison between KDE results before and after the ship routing system shows that the hot spots of ship collision candidates are reduced after the ship routing waterway is established.

Mapping Seismic Hazard for Canadian Sites Using Spatially Smoothed Seismicity Model

The estimated seismic hazard based on the delineated seismic source model is used as the basis to assign the seismic design loads in Canadian structural design codes. An alternative for the estimation is based on a spatially smoothed source model. However, a quantification of differences in the Canadian seismic hazard maps (CanSHMs) obtained based on the delineated seismic source model and spatially smoothed model is unavailable. The quantification is valuable to identify epistemic uncertainty in the estimated seismic hazard and the degree of uncertainty in the CanSHMs. In the present study, we developed seismic source models using spatial smoothing and historical earthquake catalogue. We quantified the differences in the estimated Canadian seismic hazard by considering the delineated source model and spatially smoothed source models. For the development of the spatially smoothed seismic source models, we considered spatial kernel smoothing techniques with or without adaptive bandwidth. The results indicate that the use of the delineated seismic source model could lead to under or over-estimation of the seismic hazard as compared to those estimated based on spatially smoothed seismic source models. This suggests that an epistemic uncertainty caused by the seismic source models should be considered to map the seismic hazard.

A Robust GPS Navigation Filter Based on Maximum Correntropy Criterion with Adaptive Kernel Bandwidth

Multiple forms of interference and noise that impact the receiver’s capacity to receive and interpret satellite signals, and consequently the preciseness of positioning and navigation, may be present during the processing of Global Positioning System (GPS) navigation. The non-Gaussian noise predominates in the signal owing to the fluctuating character of both natural and artificial electromagnetic interference, and the algorithm based on the minimum mean-square error (MMSE) criterion performs well when assuming Gaussian noise, but drops when assuming non-Gaussian noise. The maximum correntropy criteria (MCC) adaptive filtering technique efficiently reduces pulse noise and has adequate performance in heavy-tailed noise, which addresses the issue of filter performance caused by the presence of non-Gaussian or heavy-tailed unusual noise values in the localizing measurement noise. The adaptive kernel bandwidth (AKB) technique employed in this paper applies the calculated adaptive variables to generate the kernel function matrix, in which the adaptive factor can modify the size of the kernel width across a reasonably appropriate spectrum, substituting the fixed kernel width for the conventional MCC to enhance the performance. The conventional maximum correntropy criterion-based extended Kalman filter (MCCEKF) algorithm’s performance is significantly impacted by the value of the kernel width, and there are certain predetermined conditions in the selection based on experience. The MCCEKF with a fixed adaptive kernel bandwidth (MCCEKF-AKB) has several advantages due to its novel concept and computational simplicity, and gives a qualitative solution for the study of random structures for generalized noise. Additionally, it can effectively achieve the robust state estimation of outliers with anomalous values while guaranteeing the accuracy of the filtering.

Development of Adaptive Bandwidth Allocation Model Using PCQ for Network Performance Optimization

The number of clients has an impact on network performance, and while more bandwidth capacity can sometimes result in better network services, it is not always a given. Clients acquire the appropriate bandwidth, which is managed at the network layer by software, to control the flow and number of packets in the network. The practice of managing bandwidth includes measuring and controlling communications (traffic and packets) through network channels. This is done to prevent overburdening or overloading networks, which will lead to network congestion and poor performance. The goal of this study is to improve the internet network through bad-width allocation using the PCQ approach. When it comes to sharing bandwidth with active users and large users, the results of the suggested strategy are excellent.

Adaptive milling chatter identification based on sparse dictionary considering noise estimation and critical bandwidth analysis

As one of the most unfavorable factors, chatter will seriously hinder the improvement of production efficiency. The weak chatter identification is an effective method for stable cutting. In this paper, a weak chatter identification method is developed using sparse dictionary on account of the milling dynamic responses. As the bases for chatter identification, chatter frequencies are calculated based on the milling dynamic equations. In order to decrease false alarm rate due to the measured background noise, the noise estimation based filtering is proposed. Besides, in consideration of the parameter uncertainty, an adaptive critical bandwidth analysis is implemented with the developed energy proportion index in case of missed alarm. Then, the sparse dictionary matrix is constructed based on the obtained chatter frequencies and critical bandwidth. Finally, the gradient projection for sparse reconstruction (GPSR) algorithm is adopted for chatter frequencies extraction. The proposed method can decrease the measured background noise, obtain critical chatter band and extract weak chatter frequencies accurately. Experimental results shows that the developed method can be successful in weak chatter identification.