A micro strip patch antenna with multiple parasitic patches for Cognitive Radio Network applications is presented to enhance the bandwidth. Multiple resonances are used for the design of antenna, with a view to broaden bandwidth. A modified Koch Fractal antenna is imprinted from micro strip radiating patch. A Parasitic Strip line helps to grasp micro hertz communication through antenna. A slotted patch energized by a gap feed was established before with a large angular coverage over a bandwidth of 13.1%. In this paper, it is proposed that multiple parasitic patches are potential for cognitive radio applications where circular patch (CP) covers bandwidth of 85% with radiation pattern for Spectrum Utilization (SU) and CP with meander lines feeding behaves as communication antenna operating at Wireless Local Area Network 802.11y (3.637 GHz). The transceiver in a communication network is powered by Proposed Antenna, to acquire improved energy efficiency of 95.7%. Thus, throughput and SU have been improved, a model of antenna has been fabricated and its radiation patterns, return losses were achieved which shows fine consistency with simulated results.
The main objective of cognitive radio network is to provide flexible spectrum management, by permitting the secondary users (SUs) to temporarily access the licensed spectrum in the absence of a primary user. In the existing backward induction approach, each SU performs spectrum sensing and consequently reports the sensing details to the centralized controller. In the proposed work, an adaptive cluster-based heuristic approach (ACBHA) with cooperative spectrum sensing (CSS) scheme has been proposed for cognitive radio medium access control networks for 5G applications. To identify the availability of unused spectrum, the proposed CSS-based ACBHA algorithm adopts a cluster-based selection scheme. Simulation results show that the proposed CSS-based ACBHA provides a better result compared with existing techniques. The performance parameters such as false alarm probability, the probability of detection, spectrum sensing time, slot length, noise density have been analyzed using MATLAB R2012a.
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