Improving spectrum sensing by counting rules for cognitive radio

Spectrum sensing is crucial for the success of cognitive radio because it will guarantee service improvement to unlicensed users and avoidance of interference to licensed users. However this new radio functionality will place severe sensitivity requirement on individual radio. Local decision fusion as a low complexity method can improve the detection performance by simply increasing the number of times of observation and decision. This paper analyses fusion rules of local decision and deduces their properties and conditions under the assumption that all the decisions are independent and follow the same probability distributions. These properties and conditions can be used to increase the detection probability and to lower the false alarm probability. Finally computation examples show that the performance of local detector can be optimal when choosing appropriate decision number and fusion rule.