Wireless Sensor based Dynamic Channel Selection in Cellular Communication by Cognitive Radio Approach

In a cellular communication scenario, wireless sensors can be deployed to sense the interference power of the frequency band. In an ideal channel, interference temperature (IT) which is directly proportional to the interference power can be assumed to vary spatially with the frequency of the sub channel. We propose a scheme for approximating ITs over an extended C-band (licensed and unused television band) by fitting sub channel frequencies and corresponding ITs to a regression model. Using this model, IT of a random sub channel can be calculated by the base station (BS) for further analysis of the channel interference. Our proposed model based on readings reported by sensors helps in dynamic channel selection (S-DCS) in extended C-band for assignment to unlicensed secondary users. S-DCS maximizes channel utilization and proves to be economic from energy consumption point of view. It also exhibits substantial amount of accuracy with error bound within 6.8%. Again, users are assigned empty sub channels without actually probing them, incurring minimum delay in the process. Overall channel allocation efficiency is also maximized along with fairness to individual users