Robust Prediction of the Bandwidth of Metamaterial Antenna Using Deep Learning

The design ofmicrostrip antennas is a complex and time-consuming process,especially the step of searching for the best design parameters.Meanwhile,the performance ofmicrostrip antennas can be improved usingmetamaterial,which results in a new class of antennas called metamaterial antenna.Several parameters affect the radiation loss and quality factor of this class of antennas,such as the antenna size.Recently,the optimal values of the design parameters of metamaterial antennas can be predicted using machine learning,which presents a better alternative to simulation tools and trialand-error processes.However,the prediction accuracy depends heavily on the quality of the machine learning model.In this paper,and benefiting from the current advances in deep learning,we propose a deep network architecture to predict the bandwidth of metamaterial antenna.Experimental results show that the proposed deep network could accurately predict the optimal values of the antenna bandwidth with a tiny value of mean-square error(MSE).In addition,the proposed model is comparedwith current competing approaches that are based on support vector machines,multi-layer perceptron,K-nearest neighbors,and ensemble models.The results show that the proposed model is better than the other approaches and can predict antenna bandwidth more accurately.

Service-aware proactive vertical handoff algorithm based on bandwidth requirement prediction

Due to the heterogeneity and versatility of emerging services and applications in wireless networks,it has been a great challenge to improve the system performance with both the awareness of service characteristics and the balance of the traffic between adjacent networks.This paper is committed to solve this problem by introducing a Service-aware Proactive Vertical Handoff(SPVH) algorithm in Heterogeneous Wireless Networks(HWN).A Bandwidth Requirement Prediction Model(BRPM) is illustrated at first,which is adaptive to the system condition variants to forecast traffic requests.Moreover,by adopting a service-aware objective utility function,each user can optimize the vertical handover decisions with awareness of the related supporting networks and service characteristics.Since the decision process is executed with consideration of BRPM predictions,the SPVH algorithm can avoid congestion in HWN through a proactive method.The experiment results show that the proposed SPVH can solidly enhance the system performance in terms of service access ratio,average access delay,system throughput,usage ratio of spectrum resource,and eventually achieve higher network utility.

Novel Method of Predicting Network Bandwidth Based on Support Vector Machines

In order to solve the problems of small sample over-fitting and local minima when neural networks learn online, a novel method of predicting network bandwidth based on support vector machines(SVM) is proposed. The prediction and learning online will be completed by the proposed moving window learning algorithm(MWLA). The simulation research is done to validate the proposed method, which is compared with the method based on neural networks.