Frequency hopping patterns design based on objective optimization algorithm

Frequency hopping (FH) is a classical anti-jamming technique widely used in military and civil communication. Differential FH and cognitive FH ensure communication reliability by the relationship between current data and the previous hop's frequency and removing affected frequencies, respectively. However, these increase the Hamming autocorrelation properties and reduce available frequencies of FH patterns in the presence of wideband interference. This paper proposes a differential evolution-grey wolf optimizer-principal component analysis (DE-GWO-PCA) algorithm to optimize anti-jamming performance without altering the number of FH points while preserving excellent Hamming autocorrelation properties of the FH patterns. Compared to conventional solutions, this method shows two advantages. Firstly, the DE-GWO-PCA algorithm adjusts FH patterns to jamming environments, overcoming the limitation of traditional FH patterns that rely on fixed pseudo-random sequences. Secondly, while reducing bit error rate (BER), it addresses the drawback of traditional cognitive anti-jamming methods where spectrum sensing restricts communication to partial frequency points. Simulation results show that integrating DE-GWO-PCA with FH systems enhances anti-jamming performance while preserving superior Hamming autocorrelation properties of the FH patterns.