无人机通感一体化中基于干扰建模的多维能效方案

孙君; 徐金童

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无人机通感一体化中基于干扰建模的多维能效方案

更新时间：2026-03-03

- 无人机通感一体化中基于干扰建模的多维能效方案
- A Multi-dimensional Energy Efficiency Scheme Based on Interference Modeling in UAV Integrated Sensing and Communication
- 物联网学报 2026年
- 作者机构：
  
  南京邮电大学通信与信息工程学院，江苏南京 210003
- 作者简介：
  
  孙君，sunjun@njupt.edu.cn
- 基金信息：
  
  江苏省重点研发计划（产业前瞻与关键核心技术）(BE2022067;BE2022067-1);BE2022067-1┫
- DOI：
  中图分类号： TN92
- 收稿：2025-03-21，
  
  修回：2025-06-23，
  
  录用：2025-07-18，
- 稿件说明：
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孙君, 徐金童. 无人机通感一体化中基于干扰建模的多维能效方案[J/OL]. 物联网学报, 2026.

SUN Jun, XU Jintong. A Multi-dimensional Energy Efficiency Scheme Based on Interference Modeling in UAV Integrated Sensing and Communication[J/OL]. Chinese Journal on Internet of Things, 2026.
孙君, 徐金童. 无人机通感一体化中基于干扰建模的多维能效方案[J/OL]. 物联网学报, 2026. DOI：

SUN Jun, XU Jintong. A Multi-dimensional Energy Efficiency Scheme Based on Interference Modeling in UAV Integrated Sensing and Communication[J/OL]. Chinese Journal on Internet of Things, 2026. DOI：

摘要

无人机通感一体化（UAV - ISAC，Unmanned Aerial Vehicle Integrated Sensing and Communication）场景中，由于干扰的存在使得总传输数据速率与总功耗性能变差，本文针对该问题提出基于干扰消除的多维能量有效方案。首先建立干扰模型，全面考虑通信信号、感知信号以及感知信号与通信信号之间的干扰

本文把通信和感知总传输数据速率与总功耗之比作为能量效率优化的目标，以通信权重和感知权重为维度参数，构建多维能效函数，并提出基于粒子群优化算法（PSO，Particle Swarm Optimization）的丁克巴赫（Dinkelbach）算法优化二维权重参数来最大化能效。仿真结果表明，PSO+Dinkelbach算法的的复杂度比传统Dinkelbach算法提高了44%，能效提升了63%。不仅在能量效率(EE

Energy-Efficient)上有所提高，还能更快收敛到最优解。

Abstract

In Unmanned Aerial Vehicle Integrated Sensing and Communication (UAV - ISAC)scenarios

the overall data transmission rate and power consumption are adversely affected by interference. To address this issue

this paper proposes a multi-dimensional energy-efficient scheme based on interference mitigation. Firstly

an interference model is established that comprehensively considers the interactions among communication signals

sensing signals

and perceived signals. The objective of energy efficiency optimization is defined as the ratio of the combined data transmission rates for communication and sensing to the total power consumption. By treating communication weight and sensing weight as dimensional parameters

a multi-dimensional energy efficiency function is formulated. The Particle Swarm Optimization-enhanced Dinkelbach algorithm is employed to optimize these two-dimensional weight parameters

thereby maximizing energy efficiency. The simulation results show that the complexity of the PSO+Dinkelbach algorithm is 44% higher than that of the traditional Dinkelbach algorithm

while its energy efficiency is improved by 63%.

关键词

Keywords

references

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