Distributed communication interference resource scheduling using the master-slave parallel scheduling genetic algorithm

Zhenhua Wei

doi:10.1038/s41598-025-86478-x

Return

Distributed communication interference resource scheduling using the master-slave parallel scheduling genetic algorithm

2025-01-27

DOI：10.1038/s41598-025-86478-x

Original

Save

View PDF

Abstract

En 中文

With the increasing intelligence and diversification of communication interference in recent years, communication interference resource scheduling has received more attention. However, the existing interference scenario models have been developed mostly for remote high-power interference with a fixed number of jamming devices without considering power constraints. In addition, there have been fewer scenario models for short-range distributed communication interference with a variable number of jamming devices and power constraints. To address these shortcomings, this study designs a distributed communication interference resource scheduling model based distributed communication interference deployment and system operational hours and introduces the stepped logarithmic jamming-to-signal ratio. The proposed model can improve the scheduling ability of the master-slave parallel scheduling genetic algorithm (MSPSGA) in terms of the number of interference devices and the system's operational time by using four scheduling strategies referring to the searching number, global number, master-slave population power, and fixed-position power. The experimental results show that the MSPSGA can improve the success rate of searching for the minimum number of jamming devices by 40% and prolong the system's operational time by 128%. In addition, it can reduce the algorithm running time in the scenario with a high-speed countermeasure, the generation time of the jamming scheme, and the average power consumption by 4%, 84%, and 57%, respectively. Further, the proposed resource scheduling model can reduce the search ranges for the number of jamming devices and the system's operational time by 93% and 79%, respectively.

Keywords:

AI WEAPONS

ALLOCATION

DESIGN

AI Summary

Key information extracted from the uploaded paper, including a brief overview, abstract, background, key highlights, visual analysis, and future outlook.