Spectrum sensing for cognitive radio using quantized data fusion and Hidden Markov model

Most of the radio frequency spectrum is not being utilized efficiently. The utilization can be improved by including unlicensed users to exploit the radio frequency spectrum by not creating any interference to the primary users. For Cognitive Radio, the main issue is to sense and then identify all spectrum holes present in the environment. In this paper, we are proposing the Quantized data fusion sensing which is applied through the Hidden Markov Model (HMM). It does not need any kind of synchronizing signals from the Primary user as well as with the secondary transmitter in a working condition. Simulation results with error rates are improved by the activity of Primary User (PU) and have been presented.