A window-based optimization of energy detection in time-varying channels

Motivated by the demand and the low utilization of spectrum resource, cognitive radio appears as an alternative for the present spectrum distribution policy. Spectrum sensing is the key procedure and energy detector is the most commonly used algorithm in spectrum sensing. The communication signal often varies by time, which causes the spectrum sensing to be inaccurate. In this paper, we use window to separate detection statistic value into new and old data groups according to the sensing time. We emphasize the new data and forget the old ones at the same time in which the adverse effect caused by time-varying system would be ameliorated. Balanced factor and function are used to deal with the data. With the average risk as the criteria, we obtain lower risk using optimized method when the signal is time-varying. In addition, the size of the window, balanced factor or function could be designed to match different scenarios in order to achieve better sensing results.