Intelligent Reception of Frequency Hopping Signals Based on CVDP

Abstract

The frequency hopping communication systems have been widely used in anti-jamming communication due to their anti-interception and anti-interference capabilities. With the increasingly complex electromagnetic environment, the transmitter of the frequency hopping communication system adopts more flexible frequency hopping patterns to cope with interference. Therefore, to address the problem of traditional frequency hopping receiver systems being unable to receive properly due to intelligent frequency hopping sequence decisions made at the transmitter, we design a CVDP (CNN_VIT_Dual Multi-head Probsparse Self-attention) network based on a convolutional neural network (CNN) module and Transformer Encoder module to realize the intelligent reception of frequency hopping signals. The designed CNN module extracts the global features and spatial information of the time-frequency spectrograms and serves as the input of the VIT (Vision Transformer) network, which not only solves the problem that the VIT network is difficult to converge on small data sets but also improves the stability and robustness of the VIT network. In the Transformer Encoder module of the VIT network, a dual multi-head self-attention mechanism is used to extract critical time-frequency features and a Probsparse self-attention idea is introduced to reduce the computational complexity. Simulation results on hopping frequency estimation and received BER(Bit Error Rate) of CVDP networks in different interference and channel environments show that when a hopping sequence is unknown, CVDP-based intelligent hopping receiver systems can achieve nearly the same receiver performance as conventional reception systems in which a hopping sequence is known.