Neural Networks for Location Prediction in Mobile Networks Project

Location forecasting helps to secure the volume on the mobile air interface of networks of the radio. The choice of audible networks of the kinds of feedback and feed-forward are reviewed for the evidence to match their purpose. The forms of preferable network, learning values, input parameters, and probabilities of imitated forecasting are portrayed. The contrast with common concepts presents the benefits and losses of the application of neural networks for moveable forecasting. The output relies on the user account and the rate of excellent motions of the user.

 The mobile networks of the new trends would be planned based on smaller cells due to possible development in the amount of users and physical possibilities such as larger frequency happened with great bandwidth. The smaller cells result little place fields over maintaining the consistent paging productivity.

The less size of the fields and great amount of users result in increased signaling traffic with the aim to place the organization. The updated data of place is changed through the air interface of the cell method. The interface of air is the bottleneck under the seamless concept. This is important to decrease the signaling traffic to secure the volume over the air interface.

The great determination for signaling by the air interface is placing the updated mobile stations that are absent under the call and yet connected to the method. It is essential to decrease the traffic happened over location updating to stop the overload on the air interface.


Neural Networks for Location Prediction in Mobile Networks Project is concluded that there are a few of the supposition to decrease the traffic of signal on the radio network. One method is to utilize the movable information of the user is to forecast his place of future.

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