Benchmarking Frequency Hopping Algorithms for Resilient Wireless Communications under Multiple Jamming Environments

Frequency-hopping spread spectrum (FHSS) information and communication systems are widely used to enhance the resilience of wireless networks to interference. The effectiveness of traditional algorithms is significantly reduced in the presence of active and intelligent jammers. The aim of this manuscript is to develop an adaptive FHSS algorithm based on statistical learning of frequency-channel quality and to analyze its effectiveness compared with existing algorithms. A software simulator has been developed that implements six FHSS algorithms, five jamming models, and statistical evaluation using the Monte Carlo method based on the bit error rate, error vector magnitude, and normalized throughput metrics. The software-implemented adaptive algorithm delivered the best results among all those studied, achieving the highest overall performance metric. Compared with a system without FHSS, 64.23 % reduction in the bit error rate, 22.76 % increase in throughput and 59.32 % reduction in the error vector magnitude were achieved. The results obtained confirm the feasibility of using adaptive statistical frequency channel selection to improve the noise immunity of FHSS systems. Promising areas for further research include the modeling of intelligent jammers and the experimental verification of the implemented algorithm on computing platforms with limited resources.