基于低复杂度加法网络的非正交多址接入短报文多用户检测算法研究

王骥; 李子龙; 肖健; 李涣哲; 谢文武; 余超

doi:10.11999/JEIT231186

基于低复杂度加法网络的非正交多址接入短报文多用户检测算法研究

doi: 10.11999/JEIT231186

王骥¹,
李子龙²,
肖健¹,
李涣哲²,
谢文武^2, ,,
余超²

1.
华中师范大学物理科学与技术学院湖北武汉 430079
2.
湖南理工学院信息科学与工程学院湖南岳阳 414006

基金项目: 国家自然科学基金(62372070)，湖南省研究性创新项目(QL20230275, CX20231220)，湖南省自然科学基金(2023JJ50045)，湖南省大学生创新创业项目(S202310543040)

详细信息

作者简介:
王骥：男，副研究员，研究方向为5G/6G无线通信

李子龙：男，硕士生，研究方向为深度强化学习

肖健：男，博士生，研究方向为AI、信号处理

李涣哲：男，本科生，研究方向为信号处理

谢文武：男，副教授，研究方向为5G&6G基带算法、NFC等

余超：男，讲师，研究方向为NOMA、凸优化

通讯作者:
谢文武　gavinxie@hnist.edu.cn

中图分类号: TN911
计量
- 文章访问数: 27
- HTML全文浏览量: 10
- PDF下载量: 6
- 被引次数: 0
出版历程
- 收稿日期: 2023-10-31
- 修回日期: 2024-03-15
- 网络出版日期: 2024-04-01

Research on Multi-User Detection Algorithm for Non-Orthogonal Multiple Access Short Message Based on Low Complexity Adder Network

1.
College of Physical Science and Technology, Central China Normal University, Wuhan 430079, China
2.
School of Information Science and Engineering, Hunan Institute of Science and Technology, Yueyang 414006, China

Funds: The National Natural Science Foundation of China (62372070), The Research and Innovation Projects in Hunan Province (QL20230275, CX20231220), Hunan Provincial Natural Science Foundation (2023JJ50045), Hunan Provincial College Students’ Innovation and Entrepreneurship Projects (S202310543040)

摘要

摘要: 针对非正交多址接入(NOMA)系统中，接收机使用串行干扰删除算法译码时需要已知干扰用户的调制方式而产生额外的信令开销问题，该文提出一种基于联合星座轨迹图和深度学习的NOMA短包传输干扰用户调制方式盲检测算法。考虑在通信设备部署神经网络时存在计算复杂度高和能量消耗大等不足，将原始卷积神经网络替换为深度加法网络，在调制检测准确率，计算延迟和能耗等方面进行了充分比较，使用时域过采样技术改善低信噪比下的识别率。最后分析并验证了功率分配，数据包长度对检测性能的影响。
- 非正交多址接入 /
- 调制检测 /
- 深度加法网络 /
- 过采样
Abstract: A joint constellation trace diagram and deep learning-based blind modulation detection scheme is proposed for Non-Orthogonal Multiple Access (NOMA) systems, which can avoid the required expensive signaling overhead in successive interference cancellation algorithms, especially for NOMA-based short packet transmission. Considering the high computational complexity and energy consumption for communication equipment in the deployment of neural network, the original convolutional network is replaced by the adder network. The modulation detection accuracy, computing delay and energy consumption are fully compared for two kinds of network architectures. Meanwhile, time-domain oversampling technology is used to improve the recognition rate under low signal-to-noise ratio. Finally, the influence of power allocation and data packet length on detection performance is analyzed and verified.
- Non-Orthogonal Multiple Access (NOMA) /
- Modulation detection /
- Deep adder network /
- Oversampling

HTML全文

图 1 两用户NOMA下行链路

下载: 全尺寸图片幻灯片

图 2 UN=16QAM, UF=QPSK, $ {\alpha _{{\text{ftpc}}}} = 0.5 $时联合星座图

下载: 全尺寸图片幻灯片

图 3 UN=16QAM, UF=QPSK, $ {\alpha _{{\text{ftpc}}}} = 0.5 $, D-SNR=6 dB时联合星座轨迹图

下载: 全尺寸图片幻灯片

图 4 ${\alpha _{{\text{ftpc}}}} = 0.5$, D-SNR=6 dB时联合星座直方统计图

下载: 全尺寸图片幻灯片

图 5 调制检测网络结构

下载: 全尺寸图片幻灯片

图 6 ConvNet与AdderNet的调制识别率比较

下载: 全尺寸图片幻灯片

图 7 UN=16QAM，不同数据包长度与功率分配下调制识别率比较

下载: 全尺寸图片幻灯片

表 1 NOMA系统仿真参数

参数名	参数值
数据包符号数	100
功率衰减因子	0.5
成型滤波器	平方根升余弦
滚降系数	0.5
信道衰落	瑞利分布
多径数	3
多普勒频移	20 Hz
多普勒频谱	Jakes
目标用户	16QAM/64QAM
干扰用户	None/QPSK/16QAM/64QAM

下载: 导出CSV

表 2 AdderNet与ConvNet性能指标

网络	加法次数	乘法次数	指令延迟	能耗(pJ)	参数量
AdderNet	32.6 M	0	65.2 M	29.34 M	7.2 k
ConvNet	16.3 M	16.3 M	97.8 M	74.98 M	7.2 k

下载: 导出CSV

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