General mobility modeling and location prediction based on markovian approach constructor framework

Abstract

Nowadays, in the wireless networks the number of users and the transferred packet switched data are increasing dramatically. Due to the demands and the market competition the services are becoming more complex, therefore network providers and operators are facing even more difficult network management and operation tasks. The efficient network dimensioning and configuration highly depend on the underlying mathematical model of user distribution and expected data transfer level. In this paper we propose a Markov Movement-model Creator Framework (MMCF) for setting up a model based on the network parameters and requirements with optimal number of states. Firstly we describe a method that gives an abstract model of the mobile network and the node, and we introduce a simple classifying method that defines the necessary parameters of the exact Markov movement model. The mathematical solutions for determining these parameters are also presented in the paper. Finally we analyze the accuracy, complexity and usability of the proposed MMCF and an analytical comparison is made with other mobility models, the comparison is justified with simulations. The movement model created with the framework helps the network operators in setting up an effective authorization, fraud detection system or solving self-configurations issues.

Heterogeneous Doppler Spread-Based CSI Estimation Planning for TDD Massive MIMO

“…The cell can be partitioned into disjoint regions where the variation of the large-scale fading coefficients of the channel within each region can be considered negligible. The mobility of the users can be modeled as a Markovian model [27][28] [29], where a user can move from a location/region to its neighboring locations with a given probability (that reflects the fact that a user can moves in different directions). We consider that the position variations occur at decision times n = 0, 1, .…”