摘 要
无线通信技术的快速发展对信道估计与预测提出了更高要求，尤其是在复杂多变的传播环境中。本研究旨在深入探讨现代无线通信系统中信道估计与预测的关键技术，以提升数据传输效率和可靠性。研究首先分析了传统最小均方误差（MMSE）和最小二乘法（LS）在信道估计中的局限性，并提出一种基于深度学习的混合估计模型，该模型结合卷积神经网络（CNN）与长短期记忆网络（LSTM），能够有效捕捉信道的时间-空间相关性。通过构建大规模仿真数据集并引入实际测量数据进行验证，结果表明所提方法在低信噪比条件下仍能保持较高的估计精度，较传统方法性能提升约20%。此外，研究进一步探索了信道预测技术，提出了一种自适应预测算法，利用卡尔曼滤波与机器学习相结合的方式，实现了对未来信道状态的精准预测。
关键词：信道估计 深度学习 信道预测
Abstract
The rapid development of wireless communication technology puts forward higher requirements for channel estimation and prediction, especially in the complex and changeable transmission environment. The purpose of this study is to deeply explore the key technologies of channel estimation and prediction in modern wireless communication systems to improve the efficiency and reliability of data transmission. The paper first analyzes the limitations of the traditional minimum mean square error (MMSE) and minimum squares (LS) method in channel estimation, and proposes a hybrid estimation model based on deep learning that combines convolutional neural network (CNN) and long short-term memory network (LSTM) to effectively capture the temporal-spatial correlation of the channel. By constructing a large-scale simulation dataset and introducing actual measurement data for verification, the results show that the proposed method can still maintain high estimation accuracy under low signal to noise ratio, which is about 20% better than the traditional method. In addition, the study further explores the channel prediction technology and proposed an adaptive prediction algorithm to achieve accurate prediction of future channel states by combining Kalman filtering and machine learning.
Keyword:Channel Estimation Deep Learning Channel Prediction
目 录
1绪论 1
1.1无线通信中信道估计与预测的背景 1
1.2信道估计与预测技术的研究意义 1
1.3国内外研究现状与发展趋势 1
1.4本文研究方法与技术路线 2
2信道估计的基础理论与关键技术 2
2.1无线通信信道特性分析 2
2.2常见信道估计方法概述 3
2.3基于导频信号的信道估计技术 3
2.4信道估计中的噪声与干扰处理 4
2.5信道估计性能评估指标 4
3信道预测模型与算法研究 4
3.1信道预测的基本原理 5
3.2数据驱动的信道预测方法 5
3.3基于机器学习的信道预测模型 5
3.4动态环境下的信道预测优化 6
3.5预测精度与计算复杂度权衡 6
4实际应用中的信道估计与预测技术 7
4.1多天线系统中的信道估计与预测 7
4.2高速移动场景下的信道建模 7
4.3毫米波通信中的信道估计挑战 8
4.5实验验证与案例分析 9
结论 9
参考文献 11
致谢 12
无线通信技术的快速发展对信道估计与预测提出了更高要求，尤其是在复杂多变的传播环境中。本研究旨在深入探讨现代无线通信系统中信道估计与预测的关键技术，以提升数据传输效率和可靠性。研究首先分析了传统最小均方误差（MMSE）和最小二乘法（LS）在信道估计中的局限性，并提出一种基于深度学习的混合估计模型，该模型结合卷积神经网络（CNN）与长短期记忆网络（LSTM），能够有效捕捉信道的时间-空间相关性。通过构建大规模仿真数据集并引入实际测量数据进行验证，结果表明所提方法在低信噪比条件下仍能保持较高的估计精度，较传统方法性能提升约20%。此外，研究进一步探索了信道预测技术，提出了一种自适应预测算法，利用卡尔曼滤波与机器学习相结合的方式，实现了对未来信道状态的精准预测。
关键词：信道估计 深度学习 信道预测
Abstract
The rapid development of wireless communication technology puts forward higher requirements for channel estimation and prediction, especially in the complex and changeable transmission environment. The purpose of this study is to deeply explore the key technologies of channel estimation and prediction in modern wireless communication systems to improve the efficiency and reliability of data transmission. The paper first analyzes the limitations of the traditional minimum mean square error (MMSE) and minimum squares (LS) method in channel estimation, and proposes a hybrid estimation model based on deep learning that combines convolutional neural network (CNN) and long short-term memory network (LSTM) to effectively capture the temporal-spatial correlation of the channel. By constructing a large-scale simulation dataset and introducing actual measurement data for verification, the results show that the proposed method can still maintain high estimation accuracy under low signal to noise ratio, which is about 20% better than the traditional method. In addition, the study further explores the channel prediction technology and proposed an adaptive prediction algorithm to achieve accurate prediction of future channel states by combining Kalman filtering and machine learning.
Keyword:Channel Estimation Deep Learning Channel Prediction
目 录
1绪论 1
1.1无线通信中信道估计与预测的背景 1
1.2信道估计与预测技术的研究意义 1
1.3国内外研究现状与发展趋势 1
1.4本文研究方法与技术路线 2
2信道估计的基础理论与关键技术 2
2.1无线通信信道特性分析 2
2.2常见信道估计方法概述 3
2.3基于导频信号的信道估计技术 3
2.4信道估计中的噪声与干扰处理 4
2.5信道估计性能评估指标 4
3信道预测模型与算法研究 4
3.1信道预测的基本原理 5
3.2数据驱动的信道预测方法 5
3.3基于机器学习的信道预测模型 5
3.4动态环境下的信道预测优化 6
3.5预测精度与计算复杂度权衡 6
4实际应用中的信道估计与预测技术 7
4.1多天线系统中的信道估计与预测 7
4.2高速移动场景下的信道建模 7
4.3毫米波通信中的信道估计挑战 8
4.5实验验证与案例分析 9
结论 9
参考文献 11
致谢 12
