Journal of Generalized Lie Theory and Applications Traffic Network and Optimization a Future Subscriber ’ s Mobile Telecom Operator in Train

In this work, we studied the behavior and mobility of telecom subscribers into a train for now and predict a future telecom movement subscriber and have a perfect resources data signal of an operator mobile telecom, we used a deterministic and a probabilistic method. the train in our model example travels through a cellular network and passed into four zones (Z1, Z2, Z3 and Z4), each one is characterized by: topography, numbers of the cellular network, types of network (GSM, GPRS, UMTS ...), numbers of subscribers, types of subscribers(staffs, students, workers and others), numbers of operators (Operator 1, Operator 2 and Operator 3). We have studied statistics in deterministic and probabilistic vision of traffic the model used is approach to vehicular traffic


Introduction
We noticed that in train the telephone company cannot meet the needs of telecom subscribers (GSM, UMTS, GPRS ..) which carry a train in the path movement Kenitra-Sale-Rabat-Casa, for this we have proposed a model statistical study of the real traces of subscribers of different telecom operator (Operator1, Operator2 and Operator3) for data needed on subscribers train in this route cities: Kenitra ----> Sale ----> Rabat---> Casa. We study a real telecom subscribers traces and data of telecom operators in Morocco, and make a simulation by Matlab software by introducing the density necessary scientific and technical vision in the mobile telecom traffic.
The traffic and mobility model [1][2][3][4][5][6], is used in several operations of the cellular network, we have studied statistics for the maximum information from a subscriber to properly estimate and control the flow of a telecom operator in a given area, the study contains (region, topography, operator, types of network (GSM, GPRS, UMTS), speed, number of subscribers). for example : a subscriber telecom moves from Kenitra(Z1) --- Sale(Z2) --- Rabat(Z3) --- Casa(Z4) (areas or cities train ride) with different characteristics of each area. This model is based on the concept of cellular automata [7][8][9] which uses the density of subscribers based on hours ρ (t), this study does help operators to adjust the zones (visited, size, type, frequency of travel, fields, speed), allowing the network to control, plan and optimize a future movement of a subscriber telecom operator. By introducing the parameters required by inspiring model cellular automata (CA) [7,8,10] and traffic modeling [11,12], traffic accident [13,14], high way junctions on the neck [15], vehicle acceleration [16]. This study uses realistic conditions of the telecommunication network types with GSM, GPRS, UMTS and WIMAX (for different areas). We have studied the case of a mobile field studies with statistics (size, topography, number of operators, numbers of people, behavior, operator telecom subscribers).
We study a mathematical technique [3], simulation [4] and operating traces [17][18][19] which are based on the statement of assumptions and approximations use planning, mobility, network traffic modeling mobile (GSM, GPRS, UMTS ...) and also on the simulation requires very long computation time.
In our method all traces of actual (realistic model) and also based on the exploitation of traces [20] which allows to use the data to easily extract and exploit networks of trace data using a sample population of subscribers model [21,22] which gives a user profile with its movement in a deterministic case [23][24][25][26][27], to generalize if one follows a probability that can predict future movements of subscribers (all parameters of network traces), network planning, study area, extract (raw file) traces GSM/GPRS with the mobility of a subscriber (cell area Z1, Z2, Z3 and Z4) to properly optimize and estimate a future mobility subscribers of an operators mobile telecom ( Figure 1).

Major Transitions
It carries the traces collected by a technique based on the exploitation of the data measured using the methods [28][29][30][31][32][33][34]. We use the modeling levels of cells to calculate the transition of moving from a current state to future state ( ) It incorporates the concept area by modeling in the area.

Deterministic Case
We choose a transition parameter, chatting in the day time interval: x T x w whichhas a matrix of time: With: 1 2 3 4 , , , T T T T : time slots of stationary in 1 2 3 4 , , , Z Z Z Z .
that describes the future state of a subscriber who comes from the same area or different area.
: subscriber stationary in 1 z 21 T : subscriber moves from 2 z to 1 z in the Train.
This method is deterministic [22] means the subscriber 1 21 31 41 , , , T T T T must necessarily going to 1 z , which does not produce the actual movement of a subscriber.
To make the model realistic, we introduce probability parameters.  By exploiting the raw materials measured in GSM/GPRS BTS which can serve several cells at the same time, so one or more cells may have the same coordinates of BTS.
The difficulty of attributing a subscriber to a cell is to determine its actual location; we choose a solution that sets the position according to its profile area.

Zone Profile Creation
An area of a network can be represented as a set of cells of different coordinated to define a profile of the region we define the profile of the cell ( , ) x y using the following steps:

1)
Data extraction of cells from each zone

Integration a Density Parameter
It incorporates density as a parameter to track the movement of a subscriber or a group of subscribers and at the same time whether that subscriber moves into train in an area with sufficient resources or not.

Conclusion
We study a deterministic Mobility (subscribers in a cell in a train) by introducing a matrix of time slots of this model with the actual   calculated probability of stationarity and transition are taken into account the number of subscribers, speed, time of association and movement stages for optimization and future planning of a cellular network by the technique of centroid (coordinate system) which gives a good prediction of the future position of Morocco operator : Inwi, Meditel and Telecom with integration a density of actual traces of GSM, GPRS, UMTS, WIMAX…, which leads us to predict a future telecom subscribers and control the future data telecom mobile signal and the operator will predict the future movement of the subscribers and will see if the destination area is saturated or not in terms of resources telecom for add resources to meet the needs of future subscribers.