Dynamic Spectrum Allocation Access Using Cognitive Radio Networks in a Maritime - Dickson E Onu; Mamilus Aginwa Ahaneku; Michael O Ezea; Henry O Osuagwu; Udora Nwabuoku Nwawelu (Indonesian Journal of Electrical Engineering and Informatics (IJEEI), 2021)
The maritime environment is unique due to radio wave propagation over water, surface reflection and wave obstruction. In dealing with the challenging maritime environment, a dynamic spectrum allocation access using cognitive radio network through optimization is proposed. Existing works in this area are limited in performance due to the long duration in achieving the probability of false alarm. Matched filtering technique which is known as the optimum method for detection of primary users (PUs) faces the challenge of large power consumption as various receiver’s algorithm are needed to be executed for detection. This work provides a platform that enables minimum energy utilization by secondary users (SUs) thereby, enhancing throughput. An algorithm for throughput maximum in spectrum allocation was developed and used based on demand based model. The implementation of the developed model was carried out using Java program and the spectrum analysis using long distance path loss model and adaptive modulation code to estimate the minimum bandwidth of the secondary users. A simulation of cognitive radio mesh network for the testing and validation of the demand based algorithm preference, and also the cognitive radio network traffic was carried out using Cisco packet tracer and results shown on MATLAB. Simulation results indicate that using the demand based algorithm, the throughput rose with time and almost stabilized. This increase and steady throughput indicates effectiveness in the algorithm which shows that the PUs and SUs activities increase as holes’ detection effort varies, unlike that of genetic algorithm where the throughput rose gradually, got to a peak value at certain time and then fell which indicates instability in the variation of the throughput. Also, the average throughput of the demand based algorithm is far greater than that of genetic algorithm which shows that demand based algorithm outperforms the genetic by a far greater percentage. The percentage of optimization is approximately 26%.