A 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: after energy normalization is performed on the time-domain signal sampling vector, it is input into a single-node detection neural network, and two types of probability vectors of the existence or non-existence of the main signal are output , take the larger one, and get the single-node detection result as the signal detection result; or, after energy normalization is performed on the time-domain signal sampling vectors of different nodes, input the single-node detection neural network, and output whether the main signal exists or not The two types of probability vectors are used as the input of the multi-node cooperative detection neural network, and the multi-node detection result is obtained as the signal detection result; wherein, the single-node detection neural network is trained by a two-stage training method. The invention effectively excavates and utilizes the structural information of the modulated signal and the soft information of a single node, improves the detection performance of a single node and the cooperative detection performance of multiple nodes, and improves the above defects of the existing traditional signal detection method.

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.

Images