An adaptive link selection algorithm for cognitive cooperative network using modified bat algorithm

This paper presents an adaptive route selection algorithm that can be employed in the Cognitive Cooperative Network (CCN). Here each Cognitive node (CN), also called Secondary User (SU), communicates with other SUs and has the capability to change its transmission and receiption efficiency without interfering Primary User (PU). In this paper, we propose a modified Bat Algorithm for selecting best relay that is able to achieve considerable performance gain in CCN. The aim of the proposed approach is attaining a best link to send data, lesser packet delivery time, and higher throughput. For achieving lesser transmission time and better throughput we use Digital Network Coding (DNC) scheme along with Decode and Forward (DF) relaying protocol. The DF protocol with modified Bat algorithm improved CCN´s routing efficiency. Monte Carlo simulation is used to evaluate the performance and obtained results are compared with other protocols and performance evaluation reveals that network performance has improved in throughput.