The Study About Adaptive Threshold Improvement Algorithm Based On Energy Detection In CR Networks

In recent years, with the rapid development of information technology, people’s demand for bandwidth resources continues to rise. The limited spectrum has been unable to meet the needs of hundreds of millions of users and devices. In addition, improving the utilization of scarce spectrum has become a difficult problem that people need to solve at present. Because of the rapid and accurate detection of the ability to free spectrum, cognitive radio spectrum sensing technology has become a new method to improve the spectrum utilization rate.In this paper, we mainly study and improve spectrum sensing based on adaptive threshold. Firstly, we elaborate and contrast several traditional single-user spectrum sensing algorithms. Meanwhile, it introduces the multi-user cooperative spectrum sensing, which can alleviate the phenomenon of multipath and terminal concealment. This technique makes up for the shortage of single-user spectrum sensing algorithms, but increases the network cost.Because the traditional algorithms are mainly based on the false alarm probability to set the decision threshold, the threshold will not be able to adapt to the rapidly changing communication environment. Based on this, this paper studies and improves the two spectrum sensing optimization algorithms about adaptive decision threshold. And applied it to cooperative spectrum sensing, in order to reduce the number of collaboration users, reduce the network costs and improve the detection performance. Each secondary user adjusts the threshold dynamically according to the difference of the sensing signal-to-noise ratio, and enhances the accuracy of detection effectively and in real time. The first algorithm obtains the corresponding threshold value by calculating the minimum error probability of single-user and the whole cognitive network. And randomized the probability and time that the primary user occupies frequency band and does not occupy frequency band in detection, in order to make optimization algorithm closer to reality. The second is the enhanced adaptive threshold detection algorithm, it adjusts the detection probability and false alarm probability dynamically by using the detection parameter β. The algorithm detect the primary signals from all parties repeatedly, and choose the smallest signal fading to make the judgment result more reliable.