GNN-based optimization algorithm for joint user scheduling and beamforming

Shiwen HE; Jun YUAN; Zhenyu AN; Min ZHANG; Yongming HUANG; Yaoxue ZHANG

doi:10.11959/j.issn.1000-436x.2022133

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GNN-based optimization algorithm for joint user scheduling and beamforming

Papers | 更新时间：2024-06-05

- GNN-based optimization algorithm for joint user scheduling and beamforming
- Journal on Communications Vol. 43, Issue 7, Pages: 73-84(2022)
- 作者机构：
  
  1. 中南大学计算机学院，湖南长沙 410083
  2. 东南大学移动通信国家重点实验室，江苏南京 210096
  3. 紫金山实验室，江苏南京 211111
  4. 湖南邮电职业技术学院信息通信学院，湖南长沙 410015
  5. 清华大学计算机科学与技术系，北京 100084
- 作者简介：
- 基金信息：
  
  The National Natural Science Foundation of China(62171474);The National Natural Science Foundation of China(61720106003);The Open Research Fund of National Mobile Communications Research Laboratory of Southeast University(2022D03);OPPO Guangdong Mobile Com-munication Co., Ltd.Research Fund(CN05202112160224)
- DOI：10.11959/j.issn.1000-436x.2022133
  CLC： TN92
- Online First：2022-06，
  
  Published：25 July 2022
- 稿件说明：
移动端阅览
Shiwen HE, Jun YUAN, Zhenyu AN, et al. GNN-based optimization algorithm for joint user scheduling and beamforming[J]. Journal on Communications, 2022, 43(7): 73-84.
DOI：

Shiwen HE, Jun YUAN, Zhenyu AN, et al. GNN-based optimization algorithm for joint user scheduling and beamforming[J]. Journal on Communications, 2022, 43(7): 73-84. DOI： 10.11959/j.issn.1000-436x.2022133.

摘要

协作多点（CoMP）传输技术具有降低同频干扰和提高频谱效率的特点。对于 CoMP，用户调度与波束成形是2个分别位于媒体访问接入层和物理层的基本研究问题。在考虑用户服务质量需求下，重点研究用户调度与波束成形的联合优化问题，并以网络吞吐量最大化为目标。为了克服传统优化算法计算开销大且未有效利用网络历史数据信息的问题，提出了一种基于图神经网络联合用户调度与功率分配模型，并结合波束向量的解析公式，以实现联合用户调度与波束成形优化。仿真分析表明，所提算法能够以较低的计算开销实现与传统优化算法相匹配，甚至更优的性能表现。

Abstract

The coordinated multi-point (CoMP) transmission technology has the characteristics of reducing co-channel interference and improving spectral efficiency.For the CoMP technology

user scheduling (US) and beamforming (BF) design are two fundamental research problems located in the media access control layer and the physical layer

respectively.Under the consideration of user service quality requirements

the joint user US-BF optimization problem was investigated with the goal of maximizing network throughput.To overcome the problem that the traditional optimization algorithm had high computational cost and couldn’t effectively utilize the network historical data information

a joint US and power allocation (M-JEEPON) model based on graph neural network was proposed to realize joint US-BF optimization by combining the beam vector analytical solution.The simulation results show that the proposed algorithm can achieve the performance matching or even better than traditional optimization algorithms with lower computational overhead.

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references

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