Spectrum sensing method under non-Gaussian noise in cognitive unmanned aerial vehicle network

Abstract

The invention belongs to the technical field of spectrum sensing in a cognitive radio network, and discloses a spectrum sensing method under non-Gaussian noise in a cognitive unmanned aerial vehicle network, which comprises the following steps of: mapping an observation signal matrix by using a p norm to obtain a p norm vector; constructing a detection statistic TF based on the p norm vector; andcalculating a detection threshold eta F, and detecting a main user signal. According to the method, spectrum sensing in the cognitive unmanned aerial vehicle network can be effectively realized, and the method has good adaptability to non-Gaussian noise and random arrival of a main user. When the false alarm probability is 0.1, the detection performance of the method is higher than 80%, and the method also has good performance for Gaussian noise. Therefore, the spectrum sensing problem in the cognitive unmanned aerial vehicle network can be effectively solved.

Description

technical field [0001] The invention belongs to the technical field of spectrum sensing in cognitive radio networks, and in particular relates to a spectrum sensing method under non-Gaussian noise in a cognitive UAV network, a sensing system, computer equipment, a computer-readable storage medium, and a cognitive UAV network . Background technique [0002] With the widespread application of UAVs in military and civilian fields, UAV communication services are also growing rapidly, making limited spectrum resources increasingly scarce. In addition, the existing UAV communication spectrum management strategy is static allocation management, resulting in low spectrum utilization. To solve this problem, the Cognitive Drone Network emerges as the times require. Cognitive UAV network uses dynamic spectrum management to efficiently use idle spectrum and maximize spectrum utilization, which is an important way to alleviate the shortage of UAV communication spectrum resources. In t...

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Problems solved by technology

[0006] (2) The existing random arrival spectrum sensing method for primary users does not consider the influence of non-Gaussian noise

[0008] Non-Gaussian noise has a thick tail, usually there is no finite variance, and the primary user arrives randomly, which leads to an increase in the probability of missing alarms in existing methods

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