Resource Allocation Anti-Jamming Strategy for IM-FHSS under Mixed Jamming

Yuan Haoyuan; Shi Yuxin; Li Yusheng

doi:10.11959/j.issn.1000-0801.DXKX260519

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Resource Allocation Anti-Jamming Strategy for IM-FHSS under Mixed Jamming

更新时间：2026-09-30

- Resource Allocation Anti-Jamming Strategy for IM-FHSS under Mixed Jamming
- Telecommunications Science (2026)
- 作者机构：
  
  1.国防科技大学第六十三研究所，江苏南京 210007
  2.电磁空间安全试验评估全国重点实验室，江苏南京 210007
  3.国防科技大学电子科学学院，湖南长沙 410073
- 作者简介：
- 基金信息：
  
  Innovation Research Foundation of National University of Defense Technology (Innovation research foundation of NUDT)(ZK24-60)
- DOI：10.11959/j.issn.1000-0801.DXKX260519
  CLC： TN975
- Received：27 July 2026，
  
  Revised：2026-09-04，
  
  Accepted：10 September 2026，
- 稿件说明：
移动端阅览
Yuan Haoyuan, Shi Yuxin, Li Yusheng. Resource Allocation Anti-Jamming Strategy for IM-FHSS under Mixed Jamming[J/OL]. Telecommunications Science, 2026.
DOI：

Yuan Haoyuan, Shi Yuxin, Li Yusheng. Resource Allocation Anti-Jamming Strategy for IM-FHSS under Mixed Jamming[J/OL]. Telecommunications Science, 2026. DOI： 10.11959/j.issn.1000-0801.DXKX260519.

摘要

针对索引调制跳频扩谱（index modulation based frequency hopping spread spectrum

IM-FHSS）系统在混合干扰环境下的性能分析与资源分配问题，引入独立干扰参数对不同类型干扰进行差异化建模，基于超几何分布推导了加性高斯白噪声（additive Gaussian white noise

AWGN）和瑞利衰落信道下的完整误比特率（bit error rate

）表达式。通过定义中断概率、能量效率和资源占用率3项核心性能指标，分别建立了资源最小化和

最小化两个互补优化模型。针对优化问题的混合整数非线性、非凸多峰等复杂特征，分别采用非线性随机重用变异鲸鱼优化算法（nonlinear randomly reuse-based mutated whale optimization algorithm

NRRMWOA）和改进复合粒子群优化算法（improved composite particle swarm optimization

ICPSO）进行求解。仿真结果表明，理论

推导与蒙特卡洛仿真高度吻合，所提算法在求解精度、收敛速度和稳定性上均显著优于传统方法。

Abstract

To address the performance analysis and resource allocation challenges of index modulation-based frequency hopping spread spectrum (IM-FHSS) systems under mixed jamm

ing

independent jamming parameters were introduced to model different jamming types. Complete bit error rate (

) expressions for both additive Gaussian white noise (AWGN) and Rayleigh fading channels were derived based on hypergeometric distribution probability models. By defining 3 core performance metrics

outage probability

energy efficiency

and resource occupancy rate

two complementary optimization models

namely resource minimization and

minimization

were established. The nonlinear randomly reuse-based mutated whale optimization algorithm (NRRMWOA) and the improved composite particle swarm optimization (ICPSO) algorithm were employed to solve the two optimization problems

respectively

addressing their mixed-integer nonlinear

non-convex

and multi-modal characteristics. Simulation results demonstrated that the theoretical

derivations closely matched Monte Carlo simulations

and the proposed algorithms significantly outperformed traditional methods in terms of solution accuracy

convergence speed

and stability.

关键词

Keywords

references

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