Resource Allocation Based on Hybrid Water Filling Algorithm for Energy Efficiency Enhancement in Cognitive Radio Networks

In order to elevate the utility of licensed spectrum bands credit to coexistence within the same network of licensed or primary users and cognitive or secondary users, it is intended to have cognitive radio $(CR)theo/y$. Effective resource allocation between secondary and primary users is the most significant key aspect in this environment. In Cognitive Radio Networks (CRNs), resource allocation is used for total utilization of frequency spectrum Limitations such as transmission power, interference threshold of primary users and traffic demands of secondary users builds the challenge for maximizing the energy-efficiency. In order to overcome the problem of maximum energy consumption and minimum energy efficiency, a Hybrid Water Filling (HWF) Algorithm is proposed. Combination of Multi-Objective Particle Swarm optimization (MOPSO) and Iterative Water Filling (IWF) algorithm is baptized as HWF. MOPSO is used to optimize the sub channels and energy allocation to achieve the highest capacity under the total energy constraint. Thus by combining IWF with MOPSO, the energy efficiency is augmented and provides optimal energy transmission for resource allocation. The simulation results illustrate that the proposed algorithm could achieve the optimal energy allocation than the existing one in less amount of time.