# Intelligent Reception of Frequency Hopping Signals Based on CVDP
## 1 Introduction
Another study [14] proposed a minimum-sum decoding algorithm based on a Back Propagation (BP) network, which reduces the computational complexity of the BP algorithm and improves decoding speed. In addition to optimizing individual processing modules of the receiver with deep learning, several studies utilized deep learning to optimize multiple modules simultaneously. In [15], a fully connected network was employed to simultaneously perform equalization and decoding in multipath channels. However, its performance is worse than the minimum MSE method with known channel statistics. In [16], DNN (Deep Neural Networks) network structure was utilized to implement channel equalization and symbol detection in OFDM (Orthogonal Frequency Division Multiplexing) receivers, and experimental results demonstrate its superiority over traditional methods. To achieve end-to-end optimization of wireless communication systems, [17; 18] have investigated the use of deep learning to optimize the entire communication system, excluding RF (Radio Frequency) A/D and D/A. Additionally, an end-to-end wireless communication system using DNN was developed [19] and a conditional generative adversarial network (GAN) was used to represent channel effects and to implement key functions such as encoding, decoding, modulation, and demodulation. In [20] a deep receiver was proposed that optimally implements the entire information recovery process for conventional reception using deep neural networks, and the BER (Bit Error Rate) of the proposed deep receiver outperforms that of the conventional receiver in the presence of various factors, such as noise, power amplifier nonlinear distortion, frequency bias, multipath fading, co-channel interference, and dynamic environment. All of the above studies are related to conventional communication reception systems. In the detection of frequency hopping signals, in [21], a threshold selection method based on K-means clustering was proposed to achieve fast detection of frequency hopping signals under low signal-to-noise ratio conditions. In [22; 23], the CNN networks are used to determine the presence of frequency hopping signals. However, they cannot estimate the specific hopping frequencies. In [24], the Radial Basis Function (RBF) neural network is employed to facilitate the nonlinear prediction of Frequency Hopping (FH) sequences and to enable the discrimination of multiple FH sequence signals. In [25; 26], the LSTM networks are employed to predict frequency hopping sequences, but most of these sequences are generated using \(m\)-sequences, which are pseudo-random sequences with certain regularities. In contrast, the hopping sequences in this paper are intelligently generated by the transmitter based on different interference environments, and the deep reinforcement learning method utilized ensures that the hopping sequences are completely random. As a result, it is not possible to identify patterns from existing frequency hopping sequences for prediction. There is less research on intelligent reception of frequency hopping communication. In [27], a convolutional neural network-gated recurrent unit (CNN-GRU) network was designed to estimate multi-time slot frequency hopping sequences, and the simulation results demonstrated the network's good generalization ability and robustness. However, this method suffers from a long delay in estimating frequency hopping sequences. So we will study the estimation of the frequency point of frequency hopping communication in a one-time slot. In summary, the main contributions of this paper are as follows. * We design a CVDP network that exhibits robust generalization performance for the estimation of hopping frequency during the one-time slot. This network reduces the delay and overcomes the difficulty of training due to few features and a small data set of frequency hopping signals in the one-time slot.