Adaptive Bandwidth Research Articles

Towards self-adaptive bandwidth allocation for low-latency communications with reinforcement learning

Emerging applications such as remotely-controlled human-to-machine and tactile-haptic applications in the Internet evolution demand stringent low-latency transmission. In realising these applications, current communication networks need to reduce their latency towards a millisecond order. In our previous study, we exploited supervised learning-based machine learning techniques in analysing and optimising bandwidth allocation decisions in access networks to achieve low latency. In this paper, we propose a reinforcement learning-based solution to facilitate adaptive bandwidth allocation in access networks, without needing supervised training and prior knowledge of the underlying networks. In our proposed scheme, the central office estimates the rewards of different bandwidth decisions based on the network latency resulting from executing these decisions. The reward estimates are then used to select decisions that reduce the latency in turn. In particular, we discuss the algorithms that can be used to estimate the rewards and achieve decision selection in the proposed scheme. With extensive simulations, we analyse the performance of these algorithms in diverse network scenarios and validate the effectiveness of the proposed scheme in reducing network latency over existing schemes.

BwShare: Efficient bandwidth guarantee in cloud with transparent share adaptation

Bandwidth guarantee (BwG) is a highly desired feature in cloud data centers for enabling tenants (i.e., users) to achieve predictable performances. However, such a function currently is not commonly available in clouds since it potentially lowers the utilization efficiency of the network fabric (i.e., denoted network efficiency). The reasons lie in two aspects. First, tenants often present a time-varying and/or spatially-varying bandwidth demands. Second, the current cloud network architecture makes it hard to enable tenants to reuse unused bandwidth guarantees efficiently. To tackle these challenges, we propose BwShare, a novel bandwidth guarantee scheme in cloud data centers that can effectively improve the network efficiency at both the guaranteed level and the best-effort level in a synergized manner, while current approaches only adopt one of them. Such a design goal is achieved through two components: an adaptive bandwidth guarantee adaptation module (i.e., BGAdaptor) and a transparent work conservation module (i.e., WCEnabler). BGAdaptor improves the network efficiency at the guaranteed level by adjusting bandwidth guarantees among VMs of the same tenant based on traffic monitoring. WCEnabler works at the best-effort level by enabling VMs to reuse idled bandwidth efficiently and freely without disrupting bandwidth guarantees. The two modules can also facilitate each other’s performance. Extensive experiments show that BwShare can effectively improve the network efficiency while providing the function of bandwidth guarantee in cloud data centers.

Secure Secret Sharing With Adaptive Bandwidth in Distributed Cloud Storage Systems

The development of cloud storage technology has brought us great convenience that we can store data in remote servers and access it on any connected device. However, the frequent leakage of private data has brought more and more attention to the protection of private data. To solve related problems, the distributed storage schemes have been proposed. Considering security and fault tolerance, many of those schemes adopt threshold secret sharing techniques which are wildly used in distributed storage systems with disaster tolerance function. Nevertheless, in practical situations, the bandwidth between users and different servers may be unbalanced or even unfixed, which leads to low communication efficiency of the schemes when adopting original secret sharing. To obtain higher communication efficiency under different communication loads, we proposed a novel adaptive bandwidth secret sharing scheme in distribution cloud storage systems. In addition, we consider the scenario in which we use Shamir’s secret sharing scheme and Staircase codes respectively in order to improve the applicability of the adaptive bandwidth scheme. We make the comparative of performance analysis to show the advantages and disadvantages of these two schemes. In general, compared with the non-adaptive schemes, the proposed adaptive bandwidth scheme can make full use of unbalanced bandwidth and achieve higher average communication rate when the upper bound of bandwidth is large enough.

Probabilistic optimal power flow of AC/DC system with multiport current flow controller

To evaluate the impact of the randomness and correlation of photovoltaic (PV) and load on AC/DC systems with a multiport current flow controller (M-CFC), this paper proposes a probabilistic optimal power flow calculation for AC/DC systems based on a nonparametric kernel density estimation. First, according to the M-CFC model, the DC power flow calculation method with M-CFC was deduced, and its influence on line loss was analyzed. Second, a nonparametric kernel density estimation with an adaptive bandwidth was used to accurately describe the probability distribution of the PV and load, and correlation samples of the PV and load were obtained by the mixed copula function. Then an optimization model that considers system loss and static security was established, and a fast nondominated sorting genetic algorithm based on the elite strategy (NSGA-II) was used to calculate the multi-objective probability optimal power flow of the AC/DC system. Finally, a case study was performed on the modified IEEE39 bus system using measured PV and load data. We verified the nonparametric kernel density estimation with an adaptive bandwidth can better adapt to random component uncertainty, and M-CFC can improve the static security of the system.

An Improved Non-Parametric Method for Multiple Moving Objects Detection in the Markov Random Field

Detecting moving objects in the stationary background is an important problem in visual surveillance systems. However, the traditional background subtraction method fails when the background is not completely stationary and involves certain dynamic changes. In this paper, according to the basic steps of the background subtraction method, a novel non-parametric moving object detection method is proposed based on an improved ant colony algorithm by using the Markov random field. Concretely, the contributions are as follows: 1) A new nonparametric strategy is utilized to model the background, based on an improved kernel density estimation; this approach uses an adaptive bandwidth, and the fused features combine the colours, gradients and positions. 2) A Markov random field method based on this adaptive background model via the constraint of the spatial context is proposed to extract objects. 3) The posterior function is maximized efficiently by using an improved ant colony system algorithm. Extensive experiments show that the proposed method demonstrates a better performance than many existing state-of-the-art methods.

Bandwidth Control Mechanism and Extreme Gradient Boosting Algorithm for Protecting Software-Defined Networks Against DDoS Attacks

Software-Defined Networking (SDN) is an emerging network architecture that addresses the limitation of the traditional network by providing centralized management through a central controller that decouples the control and data planes. However, this development has made the controller a severe target for malicious users to execute attacks such as Distributed Denial of Service (DDoS) attacks. Several schemes have been proposed to mitigate DDoS attacks in SDN, but the challenges still exist. This paper proposes a DDoS mitigation scheme for SDN to ensure accurate attack detection and efficient network resource utilization. The scheme employs two stages: a bandwidth control mechanism and Extreme Gradient Boosting (XGBoost) Algorithm. The bandwidth control mechanism utilizes an adaptive bandwidth profile-based threshold and bandwidth control algorithm that trigger the XGBoost algorithm in case of threshold violations. The use of multiple bandwidth profiles in stetting the threshold ensures the threshold's adaptivity to consider the network traffic variation and reduce the packets drop ratio, which shows an outstanding result. The XGBoost algorithm classifies network traffic flow that violates a set threshold into normal or abnormal traffic. We evaluated the performance of our scheme using CICDDoS2019, NSL-KDD, and CAIDA datasets. Furthermore, we validated our proposed solution in real-time with the SDN environment. The results obtained show that our scheme protects SDN against DDoS attacks with high accuracy, low error, and efficient utilization of the network resources. The proposed system achieved 99.9% accuracy in detecting DDoS attacks with a low false-positive rate of 0.0002% in SDN.

Adaptive bandwidth control for multi-band OFDM transmission with spectrum sharing

Adaptive bandwidth control for multi-band OFDM transmission with spectrum sharing

Durağan Olmayan Uzamsal Kovaryans Yapılar İçin Uyarlanabilir Modeller

Objective: In modeling environment processes, multi- disciplinary methods are used to explain, explore and predict how the earth responds to natural human-induced environmental changes over time. Consequently, when analyzing spatial processes spatial domains, the spatial covariance of interest are always heterogeneous. However, this article proposed locally adaptive covariance for the spatial domain whose covariance is nonstationary in their spatial domain. The objectives of the study are to propose parametric, non-parametric and semi- parametric models for nonstationary spatial structure, continuous model for nonstationary spatial processes whose distance is far apart and to propose the adaptive weighting scheme approach that generates the optimal value for the nonparametric and semi-parametric models. Material and Methods: The spatial covariances are derived by applying the concept of adaptive weighting scheme approach on the covariance proposed in Nott and Dunsmuir (2002). Consequently, the local adaptive bandwidth for the nonstationary covariance was obtained for both the nonparametric and semi-parametric models. Simulations are conducted on the proposed model to examine the proposed model. Results and Conclusion: The results obtained are compared with existing models. The results indicate proposed spatial covariance are driven by the local bandwidths, penalty, weighted scheme, and tuning parameters. The adaptive models performed better in relation to existing covariances in terms of their mean square prediction errors (MSPE). The proposed models were further applied to real life Sulphate spatial data.

Spectral efficiency of adaptive OFDM systems over high mobility Nakagami-m fading channels

ABSTRACT In this paper, we investigate the impact of rate and power adaptation (RPA) and rate adaptation (RA) using square M-ary quadrature amplitude modulation (MQAM) on the average spectral efficiency (ASE) of orthogonal frequency division multiplexing (OFDM) systems under instantaneous bit-error-rate (BER) constraint over high mobility Nakagami multipath fading (NMF) channels. Especially, the closed-form expressions for the average spectral efficiency (ASE) of RPA and RA are obtained when these adaptive schemes are used over NMF channels with existence inter-carrier interference (ICI) caused by the high mobility. By comparing the ASE of RPA and RA under different number of subcarriers, a simplified rate and subcarriers bandwidth adaptation (RSBA) algorithm is obtained. Simulation results indicate that RSBA is more suitable for high-speed communication environment than RA.

A Multilevel and Spatial Analysis of the Infant and Young Child Feeding Practices and Associated Factors among the Under-2 Aged Children in Bangladesh

ABSTRACT Infant and young child feeding (IYCF) is crucial for the growth, development, and survival of a child. This study aims to examine the factors associated with IYCF practices among the under-2 aged children in Bangladesh and spatial variability. A total of 2336 children aged 6–23 months were selected from the 2014 Bangladesh Demographic and Health Survey (BDHS). A multi-level logistic regression was used to determine the association between socio-demographic and health-related factors accounting for the cluster effects. An adaptive bandwidth kernel density estimator (KDE) was used to investigate the spatial variability of IYCF practices in Bangladesh. The prevalence of minimum dietary diversity (MDD), minimum meal frequency (MMF), and minimum acceptable diet (MAD) were 29%, 47%, and 19% respectively, where the suboptimal IYCF practice was 96% among them. The study findings show that higher maternal age at childbirth and maternal education level are positively associated with MDD, MMF, and MAD. The likelihood of MMF and MAD was higher for children whose mothers had the decision-making abilities (adjusted odds ratio, [AOR] 1.23 and 1.26, respectively) and had exposure to media (AOR 1.43 and 1.38, respectively). Four or more antenatal care (ANC) visits positively associated with the MDD (AOR 1.47; 95% confidence interval [CI] 1.07–2.02) and ICFI (AOR 3.06; 95% CI 1.24–7.54). The heatmap shows Northern parts of Bangladesh performed better than South-West parts in child feeding practices. Overall, age-appropriate IYCF practices are not satisfactory, and many maternal characteristics contribute significantly to this. Geospatial inequalities are also notable across Bangladesh. The current scenario reinforces the need for programmes and interventions that address the spectrum of IYCF practices and help reduce spatial disparities.

Quality of Service in bandwidth adapted hybrid UMTS/WLAN interworking network

Integration of Universal Mobile Telecommunications System (UMTS) and Wireless Local Area Network (WLAN) result in ubiquitous connection for end users. In the integrated network, ensuring the quality of service to users and enhancing capacity of network are prominent issues. Bandwidth adaptation technique is one of the solutions to overcome these issues. Bandwidth adaptation based on per flow and per class schemes were proposed for loosely coupled interworking network. In this paper, hybrid coupled UMTS and WLAN interworking network is analyzed with bandwidth adaptation based on per flow and per class schemes and the performances have been compared. Simulation result shows that the proposed hybrid coupled interworking network with bandwidth adaptation based on per class scheme performs better with enhanced quality of service and network capacity.

Adaptive Bootstrap Design for Hybrid Terrestrial Broadcast and Mobile Communication Networks

The bootstrap in ATSC 3.0 is expected to act as a universal wake-up signal for various wireless systems in addition to broadcast network. However, the signaling decoding performance of the standardized bootstrap degrades significantly in channels of fast time-variation and strong multipath. Furthermore, part of available bandwidth is reserved to ensure the compatibility with mobile communication network (MCN), which puts a limitation on the performance improvement. In this paper, to make the best of the available bandwidth, we introduce the bandwidth-concerned version information to enable an adaptive bandwidth configuration for the proposed bootstrap. Moreover, a 2-D signaling scheme is used to increase signaling capacity by selecting different gold sequences in frequency domain (FD) and simultaneously applying cyclic shift in time domain (TD). At the receiver side, we first provide improved estimators of symbol timing offset (STO) and fine frequency offset (FFO) for the bootstrap with special TD structure. Meanwhile, a learning-based binary classifier taking the output of STO estimator as training data is provided to generate an SNR-independent threshold for spectrum sensing without requiring the knowledge of channel conditions nor noise estimator. Afterwards, an inverse fast Fourier transform (IFFT)-based algorithm is used to decode FD signaling in the presence of unknown TD signaling, which allows 3-bit higher signaling capacity. Numerical analysis and simulation results demonstrate that the proposed adaptive bootstrap design as well as corresponding receiver algorithms significantly outperform the standardized one in terms of synchronization, detection and signaling decoding.

Valence-dependent brain potentials of processing augmented feedback in learning a complex arm movement sequence.

ERPs in the EEG were scrutinized in learning a complex arm movement sequence with the aim to examine valence effects on processing augmented feedback during practice. Twenty-four healthy subjects practiced one session with 192 feedback trials according to an adaptive bandwidth feedback approach with a high informational level of feedback information (i.e., amplitude and direction of errors). The bandwidth for successful performance (increase of a score for a monetary competition) was manipulated to yield a success rate (positive feedback frequency) of approximately 50% adaptive to the current performance level. This allowed a variation of feedback valence unconfounded by success rate. In line with our hypotheses, the EEG data showed a valence-dependent feedback-related negativity (FRN) and a later fronto-central component at the FCz electrode as well as a P300 component at the Pz electrode. Moreover, the P300 and amplitudes in the FRN time window reduced in the second half of practice but were still dependent on feedback valence. Behavioral adjustments were larger after feedback with negative valence and were predicted by the late fronto-central component. The data support the assumption of feedback valence-dependent modulation of attentional cognitive involvement in motor control and learning.

A shape-based cutting and clustering algorithm for multiple change-point detection

The detection of multiple change-points without prior knowledge of the number and location of the change-points is investigated in this paper. By making full use of the geometric information of local statistics, a new change-point detection algorithm called the shape-based cutting and clustering (SCC) algorithm is established. There are three key techniques in the proposed SCC procedure: data-driven threshold, adaptive bandwidth and single peak recognition. Our simulation results show that the proposed method is highly competitive in terms of computational speed and effectiveness. In order to validate the feasibility of the proposed algorithm, we apply the methodology to an operational problem in renewable integrated electrical distribution networks. The results of the real data analysis illustrate the effectiveness of the proposed algorithm.

Multiresolution simultaneous upscaling of two features of the reservoir using the bandwidth of the kernel function and wavelet transformation

In this paper, we employ a new multiresolution upscaling method for the data in porous media. The method employs an adaptive bandwidth of the kernel function as the multiresolution upscaling operator. We have implemented a hierarchical multiresolution upscaling strategy, which is based on maintaining the important fine scale model structures of the reservoir. In this method, bandwidth is a function of the variability of cell, which controls the upscaling process. In areas with a high variability of the considered feature, small bandwidth leads to maintaining the fine scale structures; and in homogeneous reservoir areas, large bandwidths will lead to coarse-sized cells. In this paper, we have used this proposed method for simultaneous upscaling of two features: density and neutron logs. Determination of optimal bandwidth or threshold for each feature is an important challenge in the bandwidth method. Due to different variabilities of two properties or features, simultaneous upscaling should be performed to capture the variability of both features in the model. The concept of simultaneous upscaling provides a common coarse model for two properties. To prepare the SUTF model, two minimum and maximum bandwidth approaches are used. The upscaling algorithm has been applied for density and neutron logs. Although the fine scale model contains 2290 cells, the number of coarsened cells in SUTF model based on the minimum bandwidth and maximum bandwidth approaches are 402 and 110, respectively. In addition to this new proposed method, the method of wavelet transform has also been used as a well-known method for upscaling, and finally, the results of the two methods are compared. By comparing the results of the two methods, we observe that the upscaling error in the kernel bandwidth method is less than that in the wavelet transform method. The advantage of the kernel bandwidth method over the wavelet transform is the reduction of the loss of the main information as well as the reduction of the numerical dispersion in the new proposed method compared to the wavelet transform method. These benefits have appeared at the upscaling error that is less in the proposed method.

Adaptive Bandwidth Allocation Based on Sample Path Prediction With Gaussian Process Regression

Traffic prediction facilitates intelligent networking maintenance by enabling efficient network resource allocation. With the development of machine learning algorithms, traffic prediction has attracted increasing attention and has been widely used in resource allocation and traffic management. In this paper, we consider an input traffic with a quality of service (QoS) requirement, such as the overflow probability, and propose an adaptive bandwidth allocation method based on the Gaussian process regression (GPR) to satisfy the required QoS. In the proposed method with GPR, we consider the stochastic property of each sample path individually and compute the required bandwidth adaptively for each sample path by estimating its overflow probability. Thus, it is more beneficial than the bandwidth allocation method based on the average overflow probability over all sample paths that are widely used in many previous works. We investigate the computational complexity and performance of the proposed method through simulation with real-world traffic as well as computer-generated traffic and show that the proposed method allocates the bandwidth adaptively and efficiently to satisfy the required QoS.

A Reinforcement Learning Framework for Autonomous Cell Activation and Customized Energy-Efficient Resource Allocation in C-RANs

Cloud radio access networks (C-RANs) have been regarded in recent times as a promising concept in future 5G technologies where all DSP processors are moved into a central base band unit (BBU) pool in the cloud, and distributed remote radio heads (RRHs) compress and forward received radio signals from mobile users to the BBUs through radio links. In such dynamic environment, automatic decision-making approaches, such as artificial intelligence based deep reinforcement learning (DRL), become imperative in designing new solutions. In this paper, we propose a generic framework of autonomous cell activation and customized physical resource allocation schemes for energy consumption and QoS optimization in wireless networks. We formulate the problem as fractional power control with bandwidth adaptation and full power control and bandwidth allocation models and set up a Q-learning model to satisfy the QoS requirements of users and to achieve low energy consumption with the minimum number of active RRHs under varying traffic demand and network densities. Extensive simulations are conducted to show the effectiveness of our proposed solution compared to existing schemes.

Gaussian kernel adaptive filters with adaptive kernel bandwidth

Gaussian kernel adaptive filters (GKAFs) have been successfully applied in functional approximation. The kernel bandwidth for GKAFs not only impacts on the smoothness of function approximation and the locality of training samples, but also affects the convergence rate and testing accuracy. However, in practice, it is hard to predesign an optimal one. In this paper, for practical applications, we propose a novel framework for kernel bandwidth adaptation in sparsification case. In this framework, we consider the latest K kernel bandwidths as free parameters, and sequentially update them using a gradient decent method to minimize the instantaneous squared error. Furthermore, we apply the proposed method to the quantized kernel least mean square (QKLMS) algorithm, and conduct convergence analysis for the algorithm. Extensive simulation results are provided and validate the superiority of our method compared to some state-of-the-art algorithms.

Simultaneous upscaling of two properties of reservoirs in one dimension using adaptive bandwidth in kernel function method

Abstract Upscaling of primary geological models with huge cells, especially in porous media, is the first step in fluid flow simulation. Numerical methods are often used to solve the models. The upscaling method must preserve the important properties of the spatial distribution of the reservoir properties. An grid upscaling method based on adaptive bandwidth in kernel function is proposed according to the spatial distribution of property. This type of upscaling reduces the number of cells, while preserves the main heterogeneity features of the original fine model. The key point of the paper is upscaling two reservoir properties simultaneously. For each reservoir feature, the amount of bandwidth or optimal threshold is calculated and the results of the upscaling are obtained. Then two approaches are used to upscaling two properties simultaneously based on maximum bandwidth and minimum bandwidth. In fact, we now have a finalized upscaled model for both reservoir properties for each approach in which not only the number of their cells, but also the locations of the cells are equal. The upscaling error of the minimum bandwidth approach is less than that of the maximum bandwidth approach.

A Low-Pass Filter Bandwidth Adaptation Technique for Phase Interpolators

This paper presents a technique to automatically adapt the bandwidth of the low-pass filter (LPF) of the phase interpolator (PI) used in bang-bang clock and data recovery (CDR) circuits. A technique for introducing the reference nonlinearity in the presence of PI’s nonlinearity is presented. This reference is used for LPF bandwidth adaptation. A mathematical model for the PI nonlinearity and introduction of reference nonlinearity is proposed. Using this model, analysis and design of the proposed bandwidth adaptation are done. The technique is verified by simulations and Si-measurements using a full-rate bang-bang CDR, which is fabricated in 180-nm CMOS technology. The PI occupies an area of 0.014 mm2 and consumes 1.1 mA for 1-GHz PI clock frequency while working on 1.8-V supply.