多基站毫米波网络中优化广播信息年龄的多播调度算法
Multicast Scheduling Algorithm for Optimizing Broadcast Age of Information in Multi-Base Station Millimeter Wave Networks
- 物联网学报 2026年
- 作者机构：
广东工业大学计算机学院，广东 广州 510006
- 作者简介：
陈权，quan.c@gdut.edu.cn
- 基金信息：国家自然科学基金面上项目(62372118);广东省自然科学基金资助项目(2024A1515030136)
DOI：
中图分类号：收稿：2025-09-10，
修回：2025-11-10，
录用：2026-02-09，
- 稿件说明：
移动端阅览
李颖妍, 刘江龙, 李世豪, 等. 多基站毫米波网络中优化广播信息年龄的多播调度算法[J/OL]. 物联网学报, 2026.
LI Yingyan, LIU Jianglong, LI Shihao, et al. Multicast Scheduling Algorithm for Optimizing Broadcast Age of Information in Multi-Base Station Millimeter Wave Networks[J/OL]. Chinese Journal on Internet of Things, 2026.
摘要
毫米波通信凭借大带宽和低时延的优势得到了研究者广泛关注。与此同时，信息年龄（Age of Information
AoI）作为反映目的节点数据实时性的关键性能指标被提了出来。然而在毫米波网络中，针对多播调度的AoI优化机制仍缺乏充分研究。因此，针对毫米波网络中优化信息年龄的多播调度问题展开了研究，以最小化系统广播信息年龄（Broadcast Age of Information
BAoI）。首先，构建了一个包含多基站、多用户设备及多播组的系统模型，并且形式化定义了通过毫米波通信方向性选择及多基站协同调度来最小化BAoI的问题。接着，针对波束参数优化的高维非线性优化问题，提出了一种基于差分进化（Differential Evolution
DE）的元启发式多播调度算法，通过融合自适应变异策略等技术
增强算法在高维非线性搜索空间中的全局搜索能力和收敛效率。仿真实验表明，所提算法在不同系统参数设置下均能显著降低BAoI，验证了其在多基站毫米波网络中优化BAoI的有效性。
Abstract
Recently
millimeter-wave communication has attracted extensive research interests for its advantages of large bandwidth and low latency. Meanwhile
Age of Information (AoI) has been proposed as a key performance indicator reflecting the real-time performance from the point of destination. However
in millimeter-wave networks
there are still few studies on multicast scheduling mechanisms targeting AoI. Therefore
this paper mainly studies the problem of optimizing AoI in millimeter-wave networks
aiming to minimize the Broadcast Age of Information (BAoI). Firstly
this paper constructs a system model including multi-base stations
multi-user devices and multicast groups
and formulates the problem optimizing BAoI with beam selection and collaborative scheduling. For the problem of signal deafness
an efficient beam scheduling and resource allocation strategy is proposed to avoid data transmission delay caused by UE's limited single connection capability. Due to the high-dimensional nonlinear characteristics of beam parameter optimization
a multicast scheduling algorithm based on improved Differential Evolution is proposed
which integrates adaptive mutation strategies to enhance the global search ability and convergence efficiency in the high-dimensional nonlinear search space. The simulation experiments show that the proposed algorithm can significantly reduce BAoI under different system parameter settings
verifying its effectiveness in optimizing BAoI.
关键词
Keywords
references
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