An adaptive regional model spectrum sensing algorithm based on TD-LTE system

Cognitive radio is an important way to improve spectrum efficiency and spectrum sensing is the key technology of cognitive radio. However, the performance of spectrum sensing only from time–frequency dimension is insufficient. In order to get better sensing performance, an adaptive regional model spectrum sensing algorithm is proposed in this paper. Firstly, an improved region model is proposed through interference analysis. By modifying the boundary calculation method and adjusting the region sensing strategy, the proposed region model can achieve higher spectrum utilization. Secondly, the time-sharing spectrum sensing strategy and cognitive database are proposed to reduce system power consumption, and give full play to the systematic advantages of the regional model. Thirdly, an adaptive spectrum sensing algorithm is designed according to the characteristics of the regional model to realize the time-sharing spectrum sensing strategy, which ensures the consistency of the overall regional sensing performance through the optimal sampling frequency and hyperplane-distance based cooperative sensing. Finally, the spectrum prediction algorithm is used to further optimize the spectrum sensing performance. The simulation results show that the proposed regional model has higher spectrum utilization and better adaptability. Compared with other regional models, the spectrum utilization can be improved by more than 11.12% in the region with high occupancy channel and the activity range of primary users is greater than 2 km. And the control error of model classification performance can be controlled within 2.11% by sampling frequency. At the same time, the combination of spectrum prediction technology can effectively improve the spectrum sensing performance.