An Energy-Efficient Spectrum Sensing in Cognitive Radio Network Using Fractional Optimization Model
Abstract In wireless communication, the main challenge is to use the radio spectrum efficiently. The spectrum used for wireless radio technology is a natural resource that is limited and expensive. The tremendous growth of the market for wireless communication has led to radio spectrum scarcity. The process for conventional spectrum sensing initiates by scanning the frequency spectra for finding the spectrum holes. On the basis of the availability of spectrum holes, Secondary User (SU) can transmit data and need to perform periodic sensing for a seamless connection. In this work, to detect spectrum holes with improved energy utilization, we have proposed the Fractional Optimization Model (FOM) which is the combination of Gray wolf optimization and Cuckoo search algorithm to detect the spectrum holes with improved energy utilization. In this paper, the model is made to obtain energy efficiency while considering different spectrum sensing states. The energy efficiency is improved by optimizing the parameters such as transmission power, sensing bandwidth, and power spectral density using the Fractional GWO-CS optimization algorithm. With the proposed novel FOM, the spectrum holes can be detected with the optimized transmission power, sensing bandwidth, and power spectral density values. The proposed model will be implemented in the working platform of Matlab by optimizing the energy efficiency of spectrum sensing in terms of transmission power, spectrum sensing bandwidth, and power spectral density compared which will be compared with existing optimization methods.