基于深度学习的多用户MIMO-OFDM-IM系统符号检测技术
DEEP LEARNING-BASED SYMBOL DETECTION TECHNOLOGY FOR MULTI-USER OF MIMO-OFDM-IM SYSTEM
马雪娇 1袁伟娜1
作者信息
- 1. 华东理工大学信息科学与工程学院 上海 200237
- 折叠
摘要
MIMO-OFDM-IM是将MIMO-OFDM(多输入多输出正交频分复用)和IM(索引调制)结合的一种新颖的多载波调制技术.为解决目前多用户MIMO-OFDM索引调制的符号检测技术仍存在的复杂度高和误码性能受用户数量影响严重的问题,提出一种基于深度学习的符号检测框架,在该框架中,编码器与解码器都由DNN(深度神经网络)构造,采用监督式学习训练数据.实验结果表明,该框架有效地解决了以上问题.
Abstract
MIMO-OFDM-IM is a novel multicarrier modulation technique combining MIMO-OFDM(multiple-input multiple-output orthogonal frequency division multiplexing)and IM(index modulation).In order to solve the problem that the symbol detection technology of multi-user MIMO-OFDM index modulation still has high complexity and the bit error performance is seriously affected by the number of users,a symbol detection framework based on deep learning is proposed.Both encoders and decoders were constructed by DNN(deep neural network),using supervised learning training data.The experimental results show that the above problems are effectively solved.
关键词
MIMO-OFDM/索引调制/DNN/符号检测Key words
MIMO-OFDM/Index modulation/DNN/Symbol detection引用本文复制引用
出版年
2024
国家科技期刊平台
NSTL
维普
万方数据