Deep Learning Based Efficient Channel Allocation Algorithm for Next Generation Cellular Networks

TLDR: The results show that the proposed algorithm, DLCA outperforms in terms of blocking and dropping probability and the system is made learned deeply to determine the number of channels that each base station can acquire and also dynamically varying based on the time.

Abstract: The usage of mobile nodes is increasing very rapidly and so it is very essential to have an efficient channel allocation procedure for the next generation cellular networks. It is very expensive to increase the existing available spectrum. Hence, it is always better to utilize the existing spectrum in an effective way. In view of this, this paper proposes a channel allocation algorithm for next generation cellular networks which is based on deep learning. The system is made learned deeply to determine the number of channels that each base station can acquire and also dynamically varying based on the time. The originating and handoff calls are two different types of calls being considered in this paper. The number of channels that be exclusively used for originating calls and handoff calls is determined using deep learning. STWQ—Non-LA and STWQ—LAR are used to compare with the proposed work. The results show that the proposed algorithm, DLCA outperforms in terms of blocking and dropping probability.