Signal detection method based on deep learning

Abstract

The invention belongs to the field of wireless communication, and relates to a signal detection method based on deep learning, which comprises the following steps of performing energy normalization ontime domain signal sampling vectors, inputting the time domain signal sampling vectors into a single-node detection neural network, outputting two types of probability vectors that a main signal exists or does not exist, and taking the larger one to obtain a single-node detection result as a signal detection result; or, after energy normalization is carried out on the time domain signal samplingvectors of different nodes, inputting the time domain signal sampling vectors into the single-node detection neural network, outputting two types of probability vectors that a main signal exists or does not exist, and taking the two types of probability vectors as input of the multi-node cooperative detection neural network to obtain a multi-node detection result as a signal detection result, wherein the single-node detection neural network is trained through a two-stage training method. According to the method, the structured information of the modulation signal and the soft information of the single node are effectively mined and utilized, the single node detection performance and the multi-node cooperative detection performance are improved, and the defects of an existing traditional signal detection method are overcome.

Description

technical field [0001] The invention belongs to the field of wireless communication, and in particular relates to a signal detection method based on deep learning. Background technique [0002] Cognitive radio is one of the important technologies to solve the problem of spectrum scarcity, and has attracted great attention from both industry and academia. One of the characteristics of cognitive radio is that the secondary user determines whether there is an available spectrum hole by detecting the signal of the primary transmitter. The detection performance directly affects the spectral efficiency of the secondary user and whether the primary user can communicate normally. Therefore, how to achieve reliable detection with the lowest possible false alarm probability and missed detection probability at the lowest possible signal-to-noise ratio is crucial for cognitive radio development is crucial. [0003] Energy detection is a common detection technology, but in actual syste...

AI Technical Summary

Problems solved by technology

However, due to the high probability of false alarm and missed detection in the existing neural network, its application is limited.

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