Matching detection of a specific modulation method increases the complexity of the detection system. The data processed by the system is obtained after the Fast Fourier transform (FFT) block of the I/Q data at the Radio Frequency (RF) end.īlind detection is a necessary condition for the spectrum detection system. The MIMO modulation identification methods are mainly divided into: the maximum likelihood function method of the received signal, and the specific modulation features of the received signal. Among them, the researchers conducted in-depth research on space-time block code identification and MIMO modulation identification. Researchers propose an algorithm for signal identification problems specific to MIMO systems 5. Therefore, the research of signal detection methods directly affects the accuracy of spectrum detection results. Describe the spectral resources based on the signal source parameters that can greatly reduce the capacity of the spectrum database 4. For example, the location, tracking and monitoring of unregistered signals. As the number of wireless devices increases, analyzing electromagnetic spectrum detection from the perspective of source feature extraction can more effectively improve the electromagnetic spectrum resource management and control capabilities. However, these spectrum detection methods only estimate the energy distribution of the spatial spectrum and do not estimate other signal characteristics. The study of interference maps helps manage spectrum resource utilization, facilitates the rapid location of interference sources, and develops solutions 2, 3. In the spectrum detection system, most researchers pay attention to the spatial spectrum distribution prediction algorithm, spectrum occupancy calculation, and spectrum interference detection algorithm 1. This is the basis for the safe and rational use of spectrum resources from the physical layer. The electromagnetic spectrum detection system mainly realizes the perception and visualization of the spatial spectrum distribution. The use of spectrum resources by various countries is an important national development strategy. ![]() Based on the initial model gradient matrix, a reasonable algorithm is set to adjust the weight of the model, which can effectively improve the recognition rate of the modulated signal.Įlectromagnetic spectrum resources are non-renewable resources. ![]() ![]() The I/Q data sample was extracted under actual engineering conditions involving random noise, and the recognition rate dropped to approximately 79%. ![]() Experiments show that the recognition rate of the modulated signal mentioned in this paper can reach 82.75%. According to the result of the confusion matrix, the weight of the model is adjusted. The minimum gradient cumulative distance (GCD) estimate between the sample and the model is used as the decision criterion for the signal classification. A two-dimensional gradient matrix is used to describe the characteristics of the signal classification. This paper proposes to extract the distribution features of different modulated signals from the signal I/Q data. Wireless signal identification is an important part of electromagnetic spectrum detection and management activities. Electromagnetic spectrum detection is the basis of the next generation wireless communication technology.
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