Dynamic Spectrum Sensing under Crash and Byzantine Failure Environments for Distributed Convergence in Cognitive Radio Networks

In cognitive networks, efficient spectrum sensing is of great importance for communication of unlicensed secondary users (SU) without interfering with the communication of licensed primary users (PU). Such spectrum sensing requires robust and reliable communication between the SUs to sense the spectrum efficiently under different network circumstances and to make a quick decision for the data transmission. In this paper, we are proposing a decentralized cooperative algorithm for efficient sensing of spectrum in the networked cognitive radios. The proposed algorithm is investigated under crash and Byzantine failure environments to study their behavior and efficiency for consensus. Energy detector module is modeled for each cooperating SU in cognitive radio network for sensing the presence of PU in a dedicated spectrum. Moreover, SU is modeled as agents connected through undirected graphs to simulate communication among them related to the spectrum availability. Multiple simulation scenarios, based on autonomous SU using the proposed distributed consensus algorithm are presented to demonstrate the theoretical development of proposed algorithm to be visualized in real scenarios. The simulation results reveal that the proposed method provides a significant improvement in convergence rate, reliability, and in terms of various key performance indicators.