A neural network based approach for call admission control in heterogeneous networks

The next generation wireless networks will be based on infrastructure with the support of heterogeneous networks. In such a scenario, the users will be mobile between different networks; therefore the number of handovers that a user has to make will become greater. Thus, at a given instant, there will be great chance that a certain cell does not have capacity to sustain the need of users. This may result in great loss of calls and lead to poor quality of service. Moreover, in the future generation of wireless networks, end users will be able to connect any suitable network amongst available set of heterogeneous networks. This ability of an end user being connected to the network of their choice may also affect network load of various base stations. This necessitates for a suitable call admission control scheme for the implementation of heterogeneous networks in the future. Since the behavior of users arriving at any cell in heterogeneous network is unpredictable, we utilize neural network to model our heterogeneous network to admit network load, therefore the learned neural network is able to estimate when call should be admitted in a new situation. Results obtained indicate that neural network approach solves the problem of call admission control unforeseen real-time scenario. The neural network shows reduced error for the increased values of learning rate and momentum constant.