Modelling And Analyzing Link Stability In Aeronautical Ad Hoc Network

Aeronautical ad hoc network (AANET) is an application of wireless ad hoc networking inaironautical communication. It gradually becomes a new approach for future wide multimediaaeronautical communicaiton by replacing the highly-cost and long-delay satellite commulication. Asthe protocol pefermance improvement is restricted by the frequent link break-up, link stabilityanalyzing becomes a key technology in AANET research. Besides, the link stability depends much onthe mobility model it employed. Therefore, to construct a suitable mobility model is also quitenecessary in AANET study.In this paper, we introduce the Mobile ad hoc networking technology into aeronauticalcommunication, and construct a suitable mobility model for AANET, then we model and analyz thelink stability based on our proposed mobility model. The main contribution of this paper is as follows:1. We propose a mobility model suitable for AANET based on our analyzing of current mobilitymodels’ applying deficiency in AANET. In this model, we divide the motion into seven stages, i.e.,take off, rate-climb, steady-fly, turn, rate-descend, land-taxi, and pause. We then derive motionequations, steady speed distribution, and relative speed distribution based on our mobility model. Thesimulation results shows that our model not only modelling the airplane node moving, but also has thesimple-implemented and steady average speed properties.2. According to our analyzing of current works on link stability, we find link duration hasbecome an important factor in measuring link quality. This paper derived the proability functiondistribution of link duration through geometry and Markov chain theory. The former which takeadvantage of geometry theory can be applied to mobility node with line track, like a commercialairplane flying in conformity with existing trajectory. However, the latter is related more to markovchain theory, and we get the link duration distribution based on the distance transition probabilitymatrix and the link-bulit relative position distribution. As we took the Markov modelling methodwhich can process random problem effiently, the Markov chain method is more appropriate formobile node with high flexibility, such as airborn plane. In the end, we verified our theoretical resultsthrough simulation.3. We find the conclusion dirived from existing works did not match the simulation result whenanalyzing the relative position distribution in Markov chain method. So we put foward a novelmoelling method to derive the accurate result. In the end, we verify our theoretical results through simulation.