Frequency Selection Strategies for Frequency-Hopping Spread Spectrum Communication Systems Integrated with Deep Learning

Abstract

In the context of complex and ever-changing communication environments, frequency selection in frequency-hopping spread spectrum communication systems has become a crucial aspect for enhancing system performance. This paper innovatively puts forward a frequency selection strategy for frequency-hopping spread spectrum communication systems integrated with deep learning. A deep neural network model is constructed, where the Long Short-Term Memory network (LSTM) is employed to capture the sequential characteristics of the communication environment, and combined with the Convolutional Neural Network (CNN) to extract the spatial features of interference signals, fully excavating the hidden information within the data. The model training is based on historical communication data, covering diverse information such as different interference types, intensities, and spectrum utilization rates in corresponding periods. In practical applications, the currently collected environmental parameters in real time are input into the well-trained model, which promptly outputs optimized frequency selection schemes. Verified by a large number of simulation experiments, compared with the traditional frequency selection strategy based on static spectrum sensing, this strategy can reduce the probability of communication interruption by approximately 35% in scenarios with strong interference, increase the average spectrum utilization rate by 20%, and decrease the bit error rate to 40% of the original value. It effectively guarantees the stability and high efficiency of communication and provides valuable technical support for the reliable operation of modern communication systems.