# Intelligent Reception of Frequency Hopping Signals Based on CVDP
## 1 Introduction  
With the increasingly complex electromagnetic interference environment, the anti-interference capability of communication systems must meet higher and more stringent requirements to ensure the reliable transmission of information [1]. While traditional frequency hopping communication has strong anti-interference and anti-interception capabilities, complex electromagnetic interference and intelligent interference have a significant impact on its information transmission performance. Therefore, an intelligent anti-interference frequency hopping communication system is needed to cope with complex and changing interference environments and ensure normal communication between the two parties. By dynamically changing parameters such as frequency hopping sequence, hopping speed, and signals bandwidth through learning and intelligent decision-making, the transmitter achieves intelligent anti-interference based on full awareness of the electromagnetic environment. An intelligent anti-interference decision algorithm based on a Deep Q-Network with priority experience replay based on Pareto sample (PPER-DQN) for bivariate frequency hopping mode has been proposed to intelligently generate high-performance frequency hopping patterns of the frequency hopping communication system [2]. However,due to the intelligent frequency hopping decision of the transmitter, the receiver does not know the hopping pattern and cannot guarantee the proper reception of the message. Therefore, the study of intelligent reception of frequency-hopping communication holds significant theoretical significance and application value. The current research on intelligent communication reception has primarily focused on each processing module of conventional communication reception systems by using deep learning techniques to improve the performance of the communication system. In terms of channel estimation, in [3] neural networks were employed to learn the channel structure from a significant amount of training data in a large-scale Multiple-Input Multiple-Output (MIMO) system. In [4], a channel estimator utilizing deep learning was initially trained offline via simulated data, after which it was dynamically adjusted online to enhance its generalization capabilities. In [5] a deep learning-based channel estimation method was proposed under time-varying Rayleigh fading channels, which outperformed traditional methods in terms of mean square error (MSE) performance. Several studies have investigated the use of deep learning methods to address the challenges associated with channel estimation in orthogonal frequency division multiplexing (OFDM) systems [6; 7; 8]. Regarding channel equalization, an equalizer based on Multi-Layer Perceptron (MLP) networks was introduced [9], which demonstrates better error performance than traditional equalization methods. Concerning signal demodulation, convolutional neural networks were used [10] to demodulate binary phase-shift keying signals with transmission rates faster than the Nyquist rate. Furthermore, deep confidence networks and stacked self-encoders were employed [11] to implement signals demodulation for near-range multipath channels. The long short-term memory (LSTM) unit-aided intelligent DNN(Deep Neural Networks)-based deep learning (DL) demodulator for orthogonal frequency division multiplexing-aided differential chaos shift keying (OFDM-DCSK) systems was proposed [12]. In terms of channel decoding, Recurrent Neural Networks (RNNs) were utilized as decoders for convolutional codes under correlated noise [13]. It extracts sequence information of convolutional codes using a bidirectional Gate Recurrent Unit (GRU) network and calculates decoding results by a fully connected neural network. Although this approach outperforms traditional algorithms, it is not suitable for storing convolutional codes of long length.