An analytical framework in LEO mobile satellite systems servicing batched Poisson traffic

Abstract

We consider a low earth orbit (LEO) mobile satellite system (MSS) that accepts new and handover calls of multirate service-classes. New calls arrive in the system as batches, following the batched Poisson process. A batch has a generally distributed number of calls. Each call is treated separately from the others and its acceptance is decided according to the availability of the requested number of channels. Handover calls follow also a batched Poisson process. All calls compete for the available channels under the complete sharing policy. By considering the LEO-MSS as a multirate loss system with "satellite-fixed" cells, it can be analyzed via a multidimensional Markov chain, which yields to a product form solution (PFS) for the steady state distribution. Based on the PFS, we propose a recursive and yet efficient formula for the determination of the channel occupancy distribution, and consequently, for the calculation of various performance measures including call blocking and handover failure probabilities. The latter are much higher compared to the corresponding probabilities in the case of the classical (and less bursty) Poisson process. Simulation results verify the accuracy of the proposed formulas. Furthermore, we discuss the applicability of the proposed model in software-defined LEO-MSS.

Research on GRU Neural Network Satellite Traffic Prediction Based on Transfer Learning

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Probabilistic Retry and Threshold Multirate Loss Models for Impatient Calls

“…Multirate loss models (MLMs) are widely adopted in the literature for the determination of call blocking probabilities (CBP), a significant performance index in contemporary multiservice networks [1,2]. Among the MLMs, the basis is considered to be the Erlang MLM (EMLM) not only because CBP can be efficiently determined via the Kaufman-Roberts recursive formula [3,4] but also due to the fact that it has been widely adopted for the CBP computation in various types of networks (from wired [4][5][6][7][8][9][10][11][12][13][14][15][16], to optical [17][18][19][20], wireless [21][22][23][24][25][26][27][28][29][30][31][32][33][34] and even satellite networks [35][36][37][38]).…”

Congestion probabilities in OFDM wireless networks with compound Poisson arrivals

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