MULTIPLE ATTRIBUTE NETWORK SELECTION ALGORITHM BASED ON AHP AND SYNERGETIC THEORY FOR HETEROGENEOUS WIRELESS NETWORKS

It is a hot issue in communication research field to select the best network for Heterogeneous Wireless Networks(HWNs),and it is also a difficult problem to reduce the handoff number of vertical handoff.In order to solve this problem,the paper proposes a multiple attribute network selection algorithm based on Analytic Hierarchy Process(AHP)and synergetic theory.The algorithm applies synergetics to network selection,considering the candidate network as a compound system composed of multiple attribute subsystems,and combines the subsystem order degree with AHP weight to obtain entropy of the compound system,which is opposite the synergy degree of a network system.The greater the synergy degree,the better the network performance.The algorithm takes not only the coordination of objective attributes but also Quality of Service(QoS)requirements into consideration,ensuring that users select the network with overall good performance.The simulation results show that the proposed algorithm can effectively reduce the handoff number and provide uses with satisfactory QoS according to different services.

A Throughput-Aware Joint Vehicle Route and Access Network Selection Approach Based on SMDP

In intelligent transportation system(ITS), the interworking of vehicular networks(VN) and cellular networks(CN) is proposed to provide high-data-rate services to vehicles. As the network access quality for CN and VN is location related, mobile data offloading(MDO), which dynamically selects access networks for vehicles, should be considered with vehicle route planning to further improve the wireless data throughput of individual vehicles and to enhance the performance of the entire ITS. In this paper, we investigate joint MDO and route selection for an individual vehicle in a metropolitan scenario. We aim to improve the throughput of the target vehicle while guaranteeing its transportation efficiency requirements in terms of traveling time and distance. To achieve this objective, we first formulate the joint route and access network selection problem as a semi-Markov decision process(SMDP). Then we propose an optimal algorithm to calculate its optimal policy. To further reduce the computation complexity, we derive a suboptimal algorithm which reduces the action space. Simulation results demonstrate that the proposed optimal algorithm significantly outperforms the existing work in total throughput and the late arrival ratio.Moreover, the heuristic algorithm is able to substantially reduce the computation time with only slight performance degradation.

A Network Selection Scheme Based on TOPSIS in Heterogeneous Network Environment

This paper deals with network selection problem for users in heterogeneous network environment. The main context is to improve the TOPSIS( Technique for Order Preference by Similarity to Ideal Solution) network scheme by combining the network properties and the users' requirement accurately and decrease ping-pong effect. The method of entropy and FAHP( Fuzzy Analytic Hierarchy Process) are used to calculate weight value and the sojourn time calculation is used to avoid ping-pang effect. The simulation results show that the improved scheme enhances the more accuracy of network selection than the existing methods and reduces the number of ping-pang effect.

Hybrid Satellite-UAV-Terrestrial Maritime Networks:Network Selection for Users on A Vessel Optimized with Transmit Power and UAV Position

The hybrid satellite-UAV-terrestrial maritime networks have shown great promise for broadband coverage at sea.The existing works focused on vessels collaboratively served by UAV-enabled aerial base station(ABSs)and terrestrial base stations(TBSs)deployed along the coast,and proved that data rate could be improved by optimizing transmit power and ABS’s position.In practice,users on a vessel can be collaboratively served by an ABS and a vesselenabled base station(VBS)in different networks.In this case,how to select the network for users on a vessel is still an open issue.In this paper,a TBS and a satellite respectively provide wireless backhaul for the ABS and the VBS.The network selection is jointly optimized with transmit power of ABS and VBS,and ABS’s position for improving data rate of all users.We solve it by finding candidates for network selection and iteratively solving transmit power and ABS’s position for each candidate.Simulation results demonstrate that data rate can be improved by collaborative coverage for users on a vessel.

Network selection in cognitive radio enabled Wireless Body Area Networks

In recent years,the great interest in Wireless Body Area Networks(WBANs)has been aroused significantly due to the advancement in wireless communications.In wireless communication,all WBAN nodes that monitor the human body's vital functions transfer information to a central sink node,which is directly connected to a Cognitive Radio enabled Controller called CRC.To transfer this information from a CRC to an e-health server,it requires long-range wireless networks,such as UMTS,LTE,WiMAX,WiFi,and satellite internet provider.It is challenging for a CRC to select the best networks for different WBAN data traffic,such as emergency mandatory,delay sensitive,and general monitoring.This paper proposes a scheme for selecting the best network from the available networks depending on the Quality of Service(QoS)requirements for different WBAN applications.Different multiple attribute decision-making algorithms are used in the proposed scheme.Numerical results and discussion reveal that the proposed scheme is effective in making a good network selection in situations where there is a conflict among different QoS requirements for different WBAN applications.

Network selection policy in multi-radio access environment using stochastic control theory

Network selection is crucial in improving the performance of heterogeneous wireless access systems. Most of previous work on network selection or radio resource allocation concentrates on the capability of each available network and ignores the time-varying nature of wireless media due to channel fading. However, the channel condition determines the state of each wireless network and plays a vital role in ensuring quality of service in multi-radio access environment. In this article, we propose a network selection policy using stochastic control theory considering the time-varying and stochastic character of wireless channels. The proposed scheme selects one network among different alternatives in each decision epoch according to the channel state of each network, which is modeled as finite-state Markov channel, with the objectives of increasing the data-rate, decreasing the bit error rate and minishing the delay. The procedure of network selection is formulated as a stochastic control problem, which can be solved using linear programming and primal-dual index heuristic algorithm. Simulation results are presented to show that network selection has great impact on the system performance, and the proposed scheme can improve the performance significantly.

Multiplicative multi-attribute auction based optimal network selection for heterogeneous wireless networks

One of the remarkable features of the next generation network is the integration of heterogeneous wireless networks, which enables mobile users with multi-mode terminals access to the best available network seamlessly. However, most of previous work only takes account of either maximizing single user＇s utility or the whole network＇s payoff, rarely considers the negotiation between them. In this paper, we propose a novel network selection approach using improved multiplicative multi-attribute auction （MMA）. At first, an improved MMA method is put forward to define the user＇s utility. Additionally, user cost is defined by considering allocated bandwidth, network load intensity and cost factor parameter. And last the best suitable network is selected according to the user＇s performance-cost-ration. Simulation results confirm that the proposed scheme outperforms the existing scheme in terms of network selection＇s fairness, user＇s performance-cost-ration, load balancing and the number of accommodated users.

Network selection algorithm based on AHP and similarity

In heterogeneous wireless networks, there are various kinds of service demands from the users. A network selection algorithm based onthe analytic hierarchy process （AHP） and Similarity is proposed to solve this problem. The services are divided into three classes： Conversational Class, Streaming Class and Interactive Class. According to the characteristics of each service, a different judgment matrix is assigned and then the AHP method is used to calculate the network attribute weights. Taking the dynamic changes in user demands and network environment into account, a formula based on Lance distance for computing the attributes similarity is derived to evaluate the degree of conformity between user requirements and network attributes, from which the similarity between the user requirements and network attributes is calculated and then the total similarity by weighting. The network with the largest total similarity is the best choice. Simulation results demonstrate the effectiveness of the proposed scheme in improving the quality of service （QoS） according to the user requirements under three kinds of services.

Satellite Integration into 5G:Deep Reinforcement Learning for Network Selection

This paper proposes a deep-Q-network(DQN) controller for network selection and adaptive resource allocation in heterogeneous networks, developed on the ground of a Markov decision process(MDP) model of the problem. Network selection is an enabling technology for multi-connectivity, one of the core functionalities of 5G. For this reason, the present work considers a realistic network model that takes into account path-loss models and intra-RAT(radio access technology) interference. Numerical simulations validate the proposed approach and show the improvements achieved in terms of connection acceptance, resource allocation, and load balancing.In particular, the DQN algorithm has been tested against classic reinforcement learning one and other baseline approaches.

Access network selection strategy using position prediction in heterogeneous wireless networks

Access network selection(ANS)strategy is one of the most important issues in future heterogeneous networks.The current solutions for this issue are not very efficient because they do not consider the motion scenarios and cannot predict the next location for mobile node.In this paper,an effective ANS strategy based on global positioning system(GPS)is proposed.Making use of information such as position coordinates and moving velocity acquired by GPS,the ANS proposed can predict the next point of attachment for mobile node(MN),assist existing ANS strategy to make more reasonable decision,and achieve better performance.

SDN-Based Data Offloading for 5G Mobile Networks

The rapid growth of 3G/4G enabled devices such as smartphones and tablets in large numbers has created increased demand for mobile data services. Wi-Fi offloading helps satisfy the requirements of data-rich applications and terminals with improved multi- media. Wi-Fi is an essential approach to alleviating mobile data traffic load on a cellular network because it provides extra capacity and improves overall performance. In this paper, we propose an integrated LTE/Wi-Fi architecture with software-defined networking （SDN） abstraction in mobile baekhaul and enhanced components that facilitate the move towards next-generation 5G mo- bile networks. Our proposed architecture enables programmable offloading policies that take into account real-time network conditions as well as the status of devices and applications. This mechanism improves overall network performance by deriving real- time policies and steering traffic between cellular and Wi-Fi networks more efficiently.

Selective PNC in Satellite-Terrestrial Network over Non-Identical Fading Channels

We analyze the performance of a twoway satellite-terrestrial decode-and-forward(DF) relay network over non-identical fading channels.In particular,selective physical-layer network coding(SPNC) is employed in the proposed network to improve the average end-to-end throughput performance.More specifically,by assuming that the DF relay performs instantaneous throughput comparisons before performing corresponding protocols,we derive the expressions of system instantaneous bit-error-rate(BER),instantaneous end-to-end throughput,average end-to-end throughput,single node detection(SND)occurrence probability and average end-to-end BER over non-identical fading channels.Finally,theoretical analyses and Monte Carlo simulation results are presented.Evaluations show that:1) SPNC protocol outperforms the conventional physical-layer network coding(PNC) protocol in infrequent light shadowing(ILS),average shadowing(AS) and frequent heavy shadowing(FHS) Shadowed-Rician fading channels.2) As the satellite-relay channel fading gets more sewere,SPNC protocol can achieve more performance improvement than PNC protocol and the occurrence probability of SND protocol increases progressively.3) The occurrence probability increase of SND has a beneficial effect on the average end-to-end throughput in low signal-to-noise ratio(SNR) regime,while the occurrence probability decrease of SND has a beneficial effect on the average end-to-end BER in highSNR regime.

Zapp Selects ZTE to Build 3G Network in Romania

ZTE, a leading global provide of telecommunications equipment and network sulutions, announced on May 29, 2008 that it has been selected by Zapp, Romania’s fourth largest mobile operator, to roll-out a WCDMA/HSDPA commercial network. This deal reinforces ZTE position in Europe. Zapp plans on launching its 3G commercial services in early autumn 2008 and the network should be completed by 2011.

ZTE Selected by BelTelecom for GPON National Broadband Network Project

ZTE Corporation announced on May 30, 2011 that it had won BelTelecom＇s national broadband network project in Belarus.

ZTE Corporation Selected for Telkom Indonesia's CDMA Network Expansion

August 30,2007,Shenzhen,China-ZTE Corporation ("ZTE"),a leading global provider of telecommunications equipment and network solutions,announced that it has recently signed an agreement with Telkom Indonesia ("Telkom"),the largest InfoCom company and full-service network provider in Indonesia,to help expand the CDMA2000 network capacity for national coverage. Under the contract,the network expansion will cover the four main cities of the eastern part of Sulawesi,Indonesia.

一种能量高效的基于主成分分析的多接入网络选择策略(英文)

This paper brings forward a novel dynamic multiple access network selection scheme(NDMAS),which could achieve less energy loss and improve the poor adaptive capability caused by the variable network parameters.Firstly,a multiple access network selection mathematical model based on information theory is presented.From the perspective of information theory,access selection is essentially a process to reduce the information entropy in the system.It can be found that the lower the information entropy is,the better the system performance fulfills.Therefore,this model is designed to reduce the information entropy by removing redundant parameters,and to avoid the computational cost as well.Secondly,for model implementation,the Principal Component Analysis(PCA) is employed to process the observation data to find out the related factors which affect the users most.As a result,the information entropy is decreased.Theoretical analysis proves that system loss and computational complexity have been decreased by using the proposed approach,while the network QoS and accuracy are guaranteed.Finally,simulation results show that our scheme achieves much better system performance in terms of packet delay,throughput and call blocking probability than other currently existing ones.

基于SKNet的增强型Pix2pixHD图像去雾方法

针对采用去雾算法处理后图片仍存在色彩失真、残雾遗留等问题,提出一种基于SKNet的增强型Pix2pixHD去雾方法,即在增强型Pix2pixHD网络的增强模块中引入SKNet网络,实现了不同尺度特征的选择和融合,提高了算法对图像特征的利用率。此外,在SKNet网络前添加可变形卷积,使得卷积核产生自由形变,以适应不规则的目标物体,增强了模型的可变换能力,达到了很好的去雾效果。较现有的去雾方法,文章提出的网络更好地利用了图像各个尺度的特征细节,提高了图像去雾的效果。

A Cause-Selecting Control Chart Method for Monitoring and Diagnosing Dependent Manufacturing Process Stages

Many industrial products are normally processed through multiple manufacturing process stages before it becomes a final product.Statistical process control techniques often utilize standard Shewhart control charts to monitor these process stages.If the process stages are independent,this is a meaningful procedure.However,they are not independent in many manufacturing scenarios.The standard Shewhart control charts can not provide the information to determine which process stage or group of process stages has caused the problems(i.e.,standard Shewhart control charts could not diagnose dependent manufacturing process stages).This study proposes a selective neural network ensemble-based cause-selecting system of control charts to monitor these process stages and distinguish incoming quality problems and problems in the current stage of a manufacturing process.Numerical results show that the proposed method is an improvement over the use of separate Shewhart control chart for each of dependent process stages,and even ordinary quality practitioners who lack of expertise in theoretical analysis can implement regression estimation and neural computing readily.

A selective kernel-based cycle-consistent generative adversarial network for unpaired low-dose CT denoising

Low-dose computed tomography(LDCT)denoising is an indispensable procedure in the medical imaging field,which not only improves image quality,but can mitigate the potential hazard to patients caused by routine doses.Despite the improvement in performance of the cycle-consistent generative adversarial network(CycleGAN)due to the well-paired CT images shortage,there is still a need to further reduce image noise while retaining detailed features.Inspired by the residual encoder–decoder convolutional neural network(RED-CNN)and U-Net,we propose a novel unsupervised model using CycleGAN for LDCT imaging,which injects a two-sided network into selective kernel networks(SK-NET)to adaptively select features,and uses the patchGAN discriminator to generate CT images with more detail maintenance,aided by added perceptual loss.Based on patch-based training,the experimental results demonstrated that the proposed SKFCycleGAN outperforms competing methods in both a clinical dataset and the Mayo dataset.The main advantages of our method lie in noise suppression and edge preservation.