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This paper studies the effect of Device-to-Device interference in cellular networks. The focus of this study is the underlay inband interference resulting from sharing the same frequency. We propose a new mechanism that alleviates the effect of the additional interference in this hybrid system. Utilizing this interference mitigation method does not violate the frequency allocation of the cellular network. The quality of the studied network is evaluated using signal to interference plus noise ratio (SINR). The proposed approach reduces the interference levels in the uplink mode by 55%.

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Abstract-Cryptography is the one of the main categories of computer security that converts information from its normal form into an unreadable form. An Artificial Neural Network (ANN) is an information processing paradigm that is inspired by the way biological nervous systems, Neural Network (NN) has emerged over the years and has made remarkable contribution to the advancement of various fields of endeavor. The purpose of this paper is to using neural networks on Cryptography , In this paper also, we have examined and analyzed the various architectures of NN.