Spectrum sensing method based on quantum particle swarm optimization extreme learning machine

Abstract

The invention discloses a spectrum sensing method based on a quantum particle swarm optimization extreme learning machine. The method relates to the field of cognitive radio, solves the problems thatthe main user signal detection rate is low under the condition of low signal-to-noise ratio in the existing wireless channel environment, a traditional extreme learning machine algorithm is only basedon empirical risk minimization and is easy to overfit, the network structure is poor and the like, and comprises the following steps of extracting the signal cyclic spectrum characteristics and the energy characteristics; constructing a training data set; training a QPSO-ELM spectrum sensing model according to the obtained training data set; inputting the extracted energy characteristics and cyclic spectrum characteristics of the received signals into the spectrum sensing model trained in the step 3 as detection data to realize the spectrum sensing of the main user signals, and determining that a main user exists when the output of the spectrum sensing model is 1; and when the output is 0, determining that the main user does not exist. According to the method, through the optimization ofthe quantum particle swarm and the introduction of structural risks, the algorithm can extract the input features more effectively, and the false alarm probability is relatively lower.

Description

technical field [0001] The invention relates to the field of cognitive radio, in particular to a spectrum sensing algorithm based on quantum particle swarm optimization extreme learning machine. Background technique [0002] With the development of the communication industry and people's higher and higher requirements for network speed and quality, radio spectrum resources are becoming increasingly scarce. Countries allocate fixed frequency bands to fixed services based on factors such as radio service technical characteristics, service capabilities, and broadband requirements. The spectrum utilization rate is very low, and there are many idle spectrums available even in the busy frequency band. Reducing spectrum waste and improving spectrum utilization has become an urgent problem to be solved. For this reason, cognitive radio technology is proposed. With spectrum sensing technology as the core, it can quickly and accurately detect spectrum holes to realize idle spectrum ut...

AI Technical Summary

Problems solved by technology

[0005] The present invention solves the spectrum sensing problems such as the low signal-to-noise ratio detection rate of the primary user in the existing wireless channel environment, the traditional extreme learning machine algorithm is only based on empirical risk minimization, easy over-fitting, and poor network structure, etc., and provides A spectrum sensing method based on quantum particle swarm optimization extreme learning machine

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