An Improved Modeling for Network Selection Based on Graph Theory and Cost Function in Heterogeneous Wireless Systems

2019 ◽  
pp. 285-304 ◽  
Author(s):  
Mohamed Lahby ◽  
Ayoub Essouiri ◽  
Abderrahim Sekkaki
Keyword(s):  
Graph Theory ◽  
Cost Function ◽  
Packet Delay ◽  
Access Point ◽  
Vertical Handover ◽  
Mobile User ◽  
Fourth Generation ◽  
Heterogeneous Environment ◽  
Heterogeneous Wireless Systems ◽  
The Cost

The next generation of mobile wireless communications represents a heterogeneous environment which integrates variety of network generation like third generation (3G), fourth generation (4G), and fifth generation (5G). The major challenge in this heterogeneous environment is to decide which access point to use when multiple networks are available. Process of roaming mobile user from one technology to anther different is called vertical handover. In this chapter, the authors propose a new mechanism based on graph theory and cost function in order to determine the best path for the end user in terms of quality of service (QoS) when the vertical handover process is needed. Then, they investigate the impact of some existing weighting methods in order to determine the suitable method which can be coupled with the cost function. The experiments evaluation by using Mininet emulator demonstrate that the proposed approach can achieve a significant improvement concerning four QoS metrics: throughput, packet lost, packet delay, and packer jitter for two services FTP and video streaming.

Proactive link handover deploying coordinated transmission for indoor visible light communications (VLC) networks

2020 ◽  
Vol 0 (0) ◽  
Author(s):  
Cong-Nam Tran ◽  
Trong-Minh Hoang ◽  
Nam-Hoang Nguyen
Keyword(s):  
Visible Light ◽  
High Speed ◽  
Packet Delay ◽  
Access Point ◽  
Mobile User ◽  
Visible Light Communications ◽  
Light Spectrum ◽  
Transmission Method ◽  
Access Points ◽  
Handover Scheme

AbstractVisible Light Communications (VLC) is considered as an emerging technology for indoor wireless communications to achieve high-speed and secure data transmission. Instead of using radio frequency (RF) spectrum, VLC uses the visible light spectrum to perform lighting and communications functions simultaneously. Multiple access points VLC (multi-AP VLC) networks use ceiling access point (ceiling-AP) and desk access point (desk-AP) to provide both uniform and spot lighting in order to achieve full coverage and high spectral efficiency. Because mobile user equipment (UE) require seamless connectivity when moving, fast link handover between VLC access points (VLC-AP) has to be supported in the VLC networks. In this paper, we present a coordinated multi-channel transmission method (CMcT) used to improve quality of service (QoS) of cell-edge UEs and propose a novel proactive link handover scheme deploying the CMcT method for multi-AP VLC networks. Performance results obtained by computer simulation show that the proactive link handover scheme deploying the CMcT method can significantly improve user throughput and packet delay comparing with those of other link handover schemes.

Investigating the Analysis of Power Saving Mode in IEEE 802.11 for Wi-Fi Communication

2018 ◽  
Vol 6 (3) ◽  
pp. 13-19
Author(s):  
Isam Aameer Ibrahim ◽  
Haider TH Salim ◽  
Hasan F. Khazaal
Keyword(s):  
Ieee 802.11 ◽  
Network Performance ◽  
Power Saving ◽  
Packet Delay ◽  
Access Point ◽  
Battery Life ◽  
Global Issues ◽  
Time Ratio ◽  
Power Saving Mode ◽  
Saving Mode

One of the major global issues today is energy consumption. Consequently, power management was introduced in various communication technologies. For IEEE 802.11wireless communication, there is a Power Saving Mode scheme (PSM) for increase the battery life of cell phone. In this PSM, there are two key parameters: beacon period interval (BI) and listen interval(LI). In most work these values are chosen arbitrary. Here, a scheme to determine the optimal BI and LI for accomplishing the most astounding conceivable vitality proficiency is introduced. This is implemented with the application of a numerical sample to the standard IEEE 802.11 PSM and Access Point-PSM (AP-PSM) schemes. To ensure the quality of network performance analysis on the normal and change of parcel delays is doing. The well-known queuing (M/G/I) model with bulk services are utilized. After the implementation of the proposed analysis, “maximum rest plan time ratio optimal Sleep Scheme (OSS)” which is when participate stations stay in the doze mode it can be determined. In this research shows that the optimal BI and LI produce optimal OSS time ratio scheme also achieved optimal average and variance of packet delay.

Lightbeam Configuration Method and Interference Elimination Resource Scheduling for Indoor Multibeam VLC Networks

2019 ◽  
Vol 0 (0) ◽  
Author(s):  
Cong-Nam Tran ◽  
Nam-Hoang Nguyen ◽  
Trong-Minh Hoang
Keyword(s):  
Transmission Rate ◽  
Resource Scheduling ◽  
Packet Delay ◽  
Access Point ◽  
Visible Light Communications ◽  
Resource Allocation Algorithm ◽  
Downlink Transmission ◽  
High Data ◽  
Interference Elimination ◽  
Multi Access

AbstractVisible light communications (VLC) is considered as an alternative communications technology for providing indoor wireless services. VLC systems are expected to offer high data transmission rate and seamless coverage. In order to achieve these requirements, VLC systems utilizing multi-lightbeam access points (multibeam VLC-AP) for downlink transmission have been proposed recently. In this paper, we present a lightbeam configuration method and an interference elimination resource scheduling mechanism (IERS) for indoor multibeam multi-access point VLC systems. The proposed lightbeam configuration method ensures seamless connectivity between user equipment and VLC-AP. The proposed IERS mechanism consists of a beam assignment algorithm and a resource allocation algorithm for eliminating co-channel interference as well as improving system performance. Performance results obtained by computer simulation indicate that there are significant improvements in terms of downlink signal to interference plus noise ratio, user throughput and packet delay when the proposed IERS mechanism is deployed.

scholarly journals An Adaptive Optimization Method Based on Learning Rate Schedule for Neural Networks

2021 ◽  
Vol 11 (2) ◽  
pp. 850
Author(s):  
Dokkyun Yi ◽  
Sangmin Ji ◽  
Jieun Park
Keyword(s):  
Artificial Intelligence ◽  
Cost Function ◽  
Numerical Experiments ◽  
Global Minimum ◽  
Optimization Method ◽  
Learning Method ◽  
Adaptive Optimization ◽  
The Cost ◽  
Proof Of Convergence ◽  
Learning Data

Artificial intelligence (AI) is achieved by optimizing the cost function constructed from learning data. Changing the parameters in the cost function is an AI learning process (or AI learning for convenience). If AI learning is well performed, then the value of the cost function is the global minimum. In order to obtain the well-learned AI learning, the parameter should be no change in the value of the cost function at the global minimum. One useful optimization method is the momentum method; however, the momentum method has difficulty stopping the parameter when the value of the cost function satisfies the global minimum (non-stop problem). The proposed method is based on the momentum method. In order to solve the non-stop problem of the momentum method, we use the value of the cost function to our method. Therefore, as the learning method processes, the mechanism in our method reduces the amount of change in the parameter by the effect of the value of the cost function. We verified the method through proof of convergence and numerical experiments with existing methods to ensure that the learning works well.

Sorting permutations by fragmentation-weighted operations

2020 ◽  
Vol 18 (02) ◽  
pp. 2050006 ◽  
Author(s):  
Alexsandro Oliveira Alexandrino ◽  
Carla Negri Lintzmayer ◽  
Zanoni Dias
Keyword(s):  
Approximation Algorithms ◽  
Computational Biology ◽  
Cost Function ◽  
Traditional Approach ◽  
Upper Bounds ◽  
Evolutionary Distance ◽  
Lower And Upper Bounds ◽  
Approximation Factor ◽  
New Type ◽  
The Cost

One of the main problems in Computational Biology is to find the evolutionary distance among species. In most approaches, such distance only involves rearrangements, which are mutations that alter large pieces of the species’ genome. When we represent genomes as permutations, the problem of transforming one genome into another is equivalent to the problem of Sorting Permutations by Rearrangement Operations. The traditional approach is to consider that any rearrangement has the same probability to happen, and so, the goal is to find a minimum sequence of operations which sorts the permutation. However, studies have shown that some rearrangements are more likely to happen than others, and so a weighted approach is more realistic. In a weighted approach, the goal is to find a sequence which sorts the permutations, such that the cost of that sequence is minimum. This work introduces a new type of cost function, which is related to the amount of fragmentation caused by a rearrangement. We present some results about the lower and upper bounds for the fragmentation-weighted problems and the relation between the unweighted and the fragmentation-weighted approach. Our main results are 2-approximation algorithms for five versions of this problem involving reversals and transpositions. We also give bounds for the diameters concerning these problems and provide an improved approximation factor for simple permutations considering transpositions.

Maximum Likelihood Ensemble Filter: Theoretical Aspects

2005 ◽  
Vol 133 (6) ◽  
pp. 1710-1726 ◽  
Author(s):  
Milija Zupanski
Keyword(s):  
Maximum Likelihood ◽  
Data Assimilation ◽  
Cost Function ◽  
Ensemble Data Assimilation ◽  
Error Covariance ◽  
Ensemble Data ◽  
Analysis Error ◽  
Nonlinear Observation ◽  
The Cost ◽  
Maximum Likelihood Ensemble Filter

Abstract A new ensemble-based data assimilation method, named the maximum likelihood ensemble filter (MLEF), is presented. The analysis solution maximizes the likelihood of the posterior probability distribution, obtained by minimization of a cost function that depends on a general nonlinear observation operator. The MLEF belongs to the class of deterministic ensemble filters, since no perturbed observations are employed. As in variational and ensemble data assimilation methods, the cost function is derived using a Gaussian probability density function framework. Like other ensemble data assimilation algorithms, the MLEF produces an estimate of the analysis uncertainty (e.g., analysis error covariance). In addition to the common use of ensembles in calculation of the forecast error covariance, the ensembles in MLEF are exploited to efficiently calculate the Hessian preconditioning and the gradient of the cost function. A sufficient number of iterative minimization steps is 2–3, because of superior Hessian preconditioning. The MLEF method is well suited for use with highly nonlinear observation operators, for a small additional computational cost of minimization. The consistent treatment of nonlinear observation operators through optimization is an advantage of the MLEF over other ensemble data assimilation algorithms. The cost of MLEF is comparable to the cost of existing ensemble Kalman filter algorithms. The method is directly applicable to most complex forecast models and observation operators. In this paper, the MLEF method is applied to data assimilation with the one-dimensional Korteweg–de Vries–Burgers equation. The tested observation operator is quadratic, in order to make the assimilation problem more challenging. The results illustrate the stability of the MLEF performance, as well as the benefit of the cost function minimization. The improvement is noted in terms of the rms error, as well as the analysis error covariance. The statistics of innovation vectors (observation minus forecast) also indicate a stable performance of the MLEF algorithm. Additional experiments suggest the amplified benefit of targeted observations in ensemble data assimilation.

Using the cost function to generate Marshallian demand systems

2000 ◽  
Vol 25 (2) ◽  
pp. 209-227 ◽  
Author(s):  
Keith R. McLaren ◽  
Peter D. Rossitter ◽  
Alan A. Powell
Keyword(s):  
Cost Function ◽  
Demand Systems ◽  
The Cost

Optimal actuator placement in adaptive optics systems

2021 ◽  
pp. 107754632110324
Author(s):  
Berk Altıner ◽  
Bilal Erol ◽  
Akın Delibaşı
Keyword(s):  
Cost Function ◽  
Adaptive Optics ◽  
Disturbance Attenuation ◽  
Linear Quadratic ◽  
Placement Problem ◽  
Convex Optimization Problem ◽  
Wavefront Aberrations ◽  
The Cost ◽  
Actuator Placement ◽  
Quadratic Cost Function

Adaptive optics systems are powerful tools that are implemented to degrade the effects of wavefront aberrations. In this article, the optimal actuator placement problem is addressed for the improvement of disturbance attenuation capability of adaptive optics systems due to the fact that actuator placement is directly related to the enhancement of system performance. For this purpose, the linear-quadratic cost function is chosen, so that optimized actuator layouts can be specialized according to the type of wavefront aberrations. It is then considered as a convex optimization problem, and the cost function is formulated for the disturbance attenuation case. The success of the presented method is demonstrated by simulation results.

Sparse Incremental Delta-Bar-Delta for System Identification

2014 ◽  
Vol 665 ◽  
pp. 643-646
Author(s):  
Ying Liu ◽  
Yan Ye ◽  
Chun Guang Li
Keyword(s):  
System Identification ◽  
Cost Function ◽  
Learning Algorithm ◽  
Learning System ◽  
The Other ◽  
Sparse System ◽  
Speed Up ◽  
Sparse System Identification ◽  
The Cost ◽  
Zero Attractor

Metalearning algorithm learns the base learning algorithm, targeted for improving the performance of the learning system. The incremental delta-bar-delta (IDBD) algorithm is such a metalearning algorithm. On the other hand, sparse algorithms are gaining popularity due to their good performance and wide applications. In this paper, we propose a sparse IDBD algorithm by taking the sparsity of the systems into account. Thenorm penalty is contained in the cost function of the standard IDBD, which is equivalent to adding a zero attractor in the iterations, thus can speed up convergence if the system of interest is indeed sparse. Simulations demonstrate that the proposed algorithm is superior to the competing algorithms in sparse system identification.

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