基于机器学习的协作MIMO通信网络信号检测算法研究

Research on Signal Detection Algorithm of Cooperative MIMO Communication Network Based on Machine Learning

针对现代无线通信中多输入多输出(MIMO)系统面临的信号检测挑战,文中提出了一种基于机器学习的协作MIMO通信网络信号检测算法。利用深度学习技术,尤其是卷积神经网络与循环神经网络的融合模型,该研究旨在提高信号检测的精度与效率,降低计算复杂度。算法在基于大规模MIMO系统和动态无线环境的仿真测试中展现出优越性能,特别是在低信噪比条件下。研究结果验证了机器学习方法在复杂通信场景中的有效性,也为未来6G及无线通信技术的发展提供了理论与实践参考。

Aiming at the signal detection challenges faced by multiple-input multiple-output(MIMO)systems in modern wireless communication,this paper proposes a collaborative MIMO communication network signal detection algorithm based on machine learning.Using deep learning technology,especially the fusion model of convolutional neural networks and recurrent neural networks,this study aims to improve the accuracy and efficiency of signal detection and reduce the computational complexity.The algorithm exhibits superior performance in simulation tests based on large-scale MIMO systems and dynamic wireless environments,especially under low signal to noise ratio conditions.The research results verify the effectiveness of machine learning methods in complex communication scenarios,and also provide a theoretical and practical reference for the development of 6G and wireless communication technologies in the future.