An Efficient Spectrum Sensing Method Based on Compressed Sensing and Support Vector Machines
A support vector machine and compressed sensing technology, applied in the field of efficient spectrum sensing, can solve the problems of original signal reconstruction, improve the complexity of spectrum sensing, etc., and achieve the effect of reducing data dimension, improving spectrum utilization rate, and high application value
Active Publication Date: 2022-07-22
HANGZHOU DIANZI UNIV
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Among them, spectrum sensing based on compressed sensing technology has a significant effect in detection performance, but traditional compressed spectrum sensing technology needs to reconstruct the original signal, which greatly increases the complexity of spectrum sensing
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[0089]The embodiments of the present invention are described below through specific specific examples, and those skilled in the art can easily understand other advantages and effects of the present invention from the contents disclosed in this specification. The present invention can also be implemented or applied through other different specific embodiments, and various details in this specification can also be modified or changed based on different viewpoints and applications without departing from the spirit of the present invention. It should be noted that the following embodiments and features in the embodiments may be combined with each other under the condition of no conflict.
[0090] This embodiment provides an efficient spectrum sensing method based on support vector machine, such as figure 2 shown, including the following steps:
[0091] S1: Input the received signal to be sensed;
[0092] S2: obtain a measurement matrix Y through compressive sensing sampling pro...
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The invention discloses an efficient spectrum sensing method based on compressive sensing and support vector machine. Determine the diagonal elements of the sampling covariance matrix of the measurement matrix Y, and take the mean to obtain the characteristic statistic T k ; S3: Obtain the label of the received signal to be sensed by comparing the feature statistics with the threshold, and form a sample training set composed of the obtained label and feature statistics; S4: Input the sample training set into the SVM classifier for training , to obtain a spectrum classifier; S5: Input the collected data into the spectrum classifier for processing to obtain a classification result. The present invention does not need to reconstruct the original signal, and only needs to use the covariance diagonal element as the decision statistic, which greatly reduces the complexity and improves the detection probability.
Description
technical field [0001] The invention relates to the technical field of digital communication, in particular to an efficient spectrum sensing method based on compressed sensing and support vector machine. Background technique [0002] In recent years, with the rapid development of radio communication technology, the demand for radio spectrum resources has risen rapidly. In addition, because the radio spectrum resources mainly adopt a fixed allocation mode, the problems of low spectrum utilization and waste of spectrum resources are very serious. figure 1 A schematic diagram of a typical cognitive radio network (CRN) system architecture. A typical cognitive radio network (CNR) consists of a primary user (PU) and a secondary user (SU). It is usually assumed that the wireless network communication of the PU and the SU is physically separated, and the SU cannot directly obtain the channel state of the PU. In this system, the PU has the priority to use the occupied channel, and t...
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IPC IPC(8): H04B17/382G06N20/10G06K9/62
CPCH04B17/382G06N20/10G06F18/2135G06F18/2411G06F18/214
Inventor 包建荣鲁彪刘超姜斌吴俊唐向宏邱雨朱芳
Owner HANGZHOU DIANZI UNIV