Spectrum scarcity has gained a great challenge in the current scenarios of wireless communication. In order to optimize the spectrum usage on the other hand cognitive networks has shown a considerable growth. This paper tries to focus on optimization with particle swarm optimization
in cognitive networks (PSO-CN) and tree seed algorithm in cognitive networks (TSA-CN) which are multichannel based. The algorithm is based on higher probability of detection and throughput with lower probability of false alarm. The lower probability of false alarm has been achieved without
compromising on the transmission rate with TSA-CN. The convergence time is found to be quicker with TSA-CN. Results with matlab based simulator shows there is an increase in throughput and decrease in false alarm with TSA algorithm than the PSO algorithm.
Most of the existing works consider the estimation of power spectrum. However, they did not provide the implementation of power spectrum estimation.In order to provide an efficient solution, in this paper, we propose power spectrum estimation and PAPR analysis for Cognitive Radio Networks (CRN). In this technique, power spectrum value of each node is calculated by computing autocorrelation. This power spectrum value is compared with five methods namely Periodogram spectral estimate,
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