A sensing policy based on the statistical property of licensed channel in cognitive network

Many spectrum usage measurement reports have shown that the fixed-frequency allocation mechanism causes unbalanced resource occupancy. Most of the current sensing policies assume the same utilisation rate for various channels. Therefore, their performance cannot be optimised in the presence of sensing constraints. This paper proposes a modified sensing policy based on the statistical property of licensed channels. Using the negotiation rule and the statistics sensing results for the perception phase, the proposed approach can always select the licensed channels with the lowest statistical occupation number. The probability statistics approach is used to formulate the proposed sensing policies for the saturation network. Both analytical and simulation results are presented to validate the proposed model. The results show that our proposed sensing policy can maintain sensing efficiency without adding constraints and also guarantee that more available licensed channels are available. In addition, the computational cost, i.e., sensing number and time, can be reduced. We conclude that our proposed sensing policy can make full use of spectrum resources to improve network throughput.