基于深度学习的下行大规模MIMO OFDM系统的1比特预编码算法  

1-bit Precoding Algorithm for Massive MIMO OFDM Downlink Systems with Deep Learning

作　　者：周宸颢 温利嫄 钱骅[1] 康凯[1] ZHOU Chenhao;WEN Liyuan;QIAN Hua;KANG Kai(Shanghai Advanced Research Institute,Chinese Academy of Sciences,Shanghai 201210,China;University of Chinese Academy of Sciences,Beijing 100049,China;School of Information Science and Technology,ShanghaiTech University,Shanghai 201210,China)

机构地区：[1]中国科学院上海高等研究院,上海201210 [2]中国科学院大学,北京100049 [3]上海科技大学信息科学与技术学院,上海201210

出　　处：《电子与信息学报》2024年第3期886-894,共9页Journal of Electronics & Information Technology

基　　金：国家重点研究发展计划(2020YFB2205603);国家自然科学基金(61971286)。

摘　　要：大规模多输入多输出(MIMO)系统中通过在基站端配备数百根天线,在提高频谱利用效率的同时,也带来了系统成本的增加。本课题组之前提出了一种适用于下行大规模MIMO正交频分复用(OFDM)系统的收敛保证的多载波1比特预编码算法(CG-MC1bit),能够获得较优的系统性能,但相应的计算复杂度较高,阻碍了其在实时系统中的应用。为进一步解决大规模MIMO系统中的成本和功耗问题,该文提出了一个模型驱动的神经网络,在CG-MC1bit算法的基础上迭代展开(Unfolding)得到了一种更加高效的CG-MC1bit-Net算法。具体而言,将迭代算法展开为一个神经网络,并引入可训练的参数来替代前向传播中的高复杂性操作。实验结果表明,该方法能够自动更新参数,与传统的预编码算法相比,收敛速度更快,计算复杂度更低。The base station of a massive Multiple-Input Multiple-Output(MIMO)system is equipped with hundreds of antennas,enhancing the spectral efficiency of the system and increasing the system costs.To address this problem,our research group proposed a Convergence-Guaranteed Multi-Carrier one-bit precoding(CG-MC1bit)iterative algorithm suitable for Orthogonal Frequency-Division Multiplexing(OFDM)downlink massive MIMO systems,which can ensure superior system performance.However,the corresponding computational complexity is high,hindering the practical application of the algorithm in real-time systems.To address this issue,we propose a model-driven,unfolding neural network,which is based on the CG-MC1bit iterative algorithm and introduces trainable parameters to replace high-complexity operations in forward propagation.In particular,we unfold the iterative algorithm into a neural network and introduce trainable parameters to replace high-complexity operations in forward propagation.Simulation results reveal that this method can automatically update parameters.In addition,compared with the traditional precoding algorithms,the proposed method has a higher convergence speed and lower computational complexity.

关 键 词：大规模多输入多输出 预编码 正交频分复用(OFDM) 迭代展开 神经网络 

分 类 号：TN929.5[电子电信—通信与信息系统] TP181[电子电信—信息与通信工程]