Throughput optimization using availability analysis based spectrum sensing for a cognitive radio

Recently, cognitive radio has been projected to facilitate the utilization of vacant licensed frequency bands through dynamic spectrum access. Availability analysis of frequency bands may be considered necessary to avoid wastage of time to sense already occupied bands. In this article, we propose availability analysis by modeling the trend of state transition of a frequency band from occupied to vacant or vice versa as a two-state Semi-Markov Process. The state transition rates are treated as random variables and their estimates are found by Bayesian approach. We start the analysis with different prior distributions and find out respective posterior distributions of the state transition rates. Subsequently, Bayes' risk has been calculated using several loss functions and estimates are found out by minimizing it. Average availability is then calculated over a time horizon that consists of a number of time frames. Each frame is divided into sensing and transmission slots. Initially, a frame length has been calculated under constant false alarm and detection rates to maximize the achievable throughput. Next, the sensing time in a frame has been calculated under constant detection rate and average availability for maximizing the achievable throughput. Numerical results are provided to substantiate the findings.