Research On The Key Technologies Of Routing And Traffic Control For Satellite Networks

Satellite networks have become an effective way to realize global ubiquitous networks because of their characteristics of wide coverage,easy access and being unlimited by geographical conditions.With the rapid development over the past 20 years,the applications of satellite networks have been extended from the traditional areas of military operations and TV broadcasting to multimedia broadcasting,meteorological monitoring,location and navigation,telemedicine,long-distance education,high-speed Internet access and others.Routing and traffic control,as the core of any communication protocol,play a vital role in the development of various services offered over satellite networks.However,the characteristic of high failure risk of satellites and the rapid expansion of global satellite traffic pose continuous challenges for routing and traffic control in satellite networks.To improve the routing and traffic control capabilities of satellite networks,three relevant topics are studied in-depth in this paper.The main contents and contributions of the reported studies are as follows:(1)Analyzing the survivabilities of satellite network routing algorithms and proposing a new evaluation index for error paths.As the core of satellite networks,routing algorithms attract considerable attention from industry and academia.Although much research has been done on the performance of satellite network routing algorithms under normal conditions,their performance under node failure conditions is seldom studied.In this study,a number of tests are designed to evaluate the average delay and packet loss rate of mainstream routing algorithms for satellite networks including SPF,DRA and SGRP.The key feature of these tests is that they focus on the performance under single-node and multiple-node failure scenarios.Moreover,a novel error path evaluation index is proposed to compensate the traditional statistical indicators for analyzing the instantaneous convergence performance of these routing algorithms.Experimental results show that this research can correctly reveal many important survivability characteristics of routing algorithms for satellite networks,while the proposed error path evaluation index can make up for the shortcomings of traditional indexes to enable more complete evaluation of routing algorithms.(2)Proposing a novel hybrid global-local(HGL)load balancing routing scheme for satellite networks.Because of their varying user traffic density,satellite networks are subject to imbalanced traffic requirements and frequent link congestion.Existing load balancing schemes have the serious problem that they are excessively dependent on a single load balancing strategy.To effectively solve this problem and achieve the optimal transmission of satellite traffic,a novel hybrid global-local(HGL)load balancing routing scheme is proposed for LEO satellite networks in this study.The HGL scheme can optimally allocate network traffic by combining the merits of both global and local strategies.In the HGL scheme,given the predictive nature of LEO satellite networks,the inter-satellite traffic demand is decomposed into a predictable long-range baseline(slowly changing)and unpredictable short-range fluctuations(changing rapidly and in real time).A global strategy is employed first for preliminary optimal traffic allocation based on the long-range baseline,and a local strategy is then employed for route adjustments based on the real-time fluctuations.Through this combination of global planning and local real-time adjustments,near-optimal allocation of the network traffic can eventually be achieved.Numerical simulations indicate that the HGL scheme is suitable for a satellite environment and that it can more thoroughly eliminate congestion and reduce the packet loss rate,average packet delay,route oscillations and communication overhead compared with single-strategy schemes.(3)Proposing a short-term traffic forecasting algorithm based on principal component analysis and a generalized regression neural network for satellite networks.Traffic forecasting is vital for improving the data transmission efficiency,resource management and quality of service in satellite networks.It can help to transform the traffic control model from a passive response model to an active perception model.To precisely forecast the short-term traffic loads in satellite networks,a forecasting algorithm based on principal component analysis and a generalized regression neural network(PCA-GRNN)is proposed in this study.The PCA-GRNN algorithm exploits the hidden regularity of satellite networks and fully considers both the temporal and spatial correlations of satellite traffic.Specifically,it selects optimal spatio-temporally correlated historical traffic data from satellites for use as forecasting inputs and applies principal component analysis to reduce the dimensions of the input data.Then,a generalized regression neural network is utilized to perform short-term traffic forecasting based on the obtained principal components.The proposed algorithm is evaluated based on real-world traffic traces,and the results show that the proposed PCA-GRNN method achieves a higher forecasting accuracy,has a shorter training time and is more robust than other state-of-the-art algorithms,even for incomplete traffic datasets.Therefore,the PCA-GRNN algorithm can be regarded as a preferred solution for use in real-time traffic forecasting for realistic satellite networks.