Abstract: Noise degrades the overall efficiency of the data transmission in the networking models which is no different in Cognitive Radio Adhoc Networks (CRAHNs). For efficient opportunistic routing in CRAHN, the Modified SMOR (M-SMOR) and Sparsity based Distributed Spectrum Map M-SMOR (SDS-M-SMOR) have been developed which provide significant improvement in the overall routing behavior. However, the increase in the noises is inevitable especially in large scale networks which Swarm Optimization (PSO) and Genetic Algorithm (GA) together termed as HPSOGA. The proposed HPSOGA based adaptive filter readjusts the filter constraints in accordance to the channel and the signals, thus mitigates the noise in the reconfigurable systems, like CRAHNs. The key benefit of the HPSOGA based adaptive filter is the global optimization when compared to other, the proposed model with noise cancellation has better performance values than other routing models.

Adnan Alrabea, “Using Hybrid Evolutionary Algorithm based Adaptive Filtering” International Journal of Advanced Computer Science and Applications(IJACSA), 8(9), 2017. http://dx.doi.org/10.14569/IJACSA.2017.080936

@article{Alrabea2017,
title = {Using Hybrid Evolutionary Algorithm based Adaptive Filtering},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2017.080936},
url = {http://dx.doi.org/10.14569/IJACSA.2017.080936},
year = {2017},
publisher = {The Science and Information Organization},
volume = {8},
number = {9},
author = {Adnan Alrabea}
}