中圖分類號(hào)： TN92 文獻(xiàn)標(biāo)識(shí)碼： A DOI：10.16157/j.issn.0258-7998.211341 中文引用格式： 蒲旭敏,，吳超,，楊小瓏. 基于深度學(xué)習(xí)的1-比特超大規(guī)模MIMO信道估計(jì)[J].電子技術(shù)應(yīng)用，2021,，47(8)：87-90,，96. 英文引用格式： Pu Xumin，Wu Chao,，Yang Xiaolong. Channel estimation for 1-bit extremely massive MIMO via deep learning[J]. Application of Electronic Technique,，2021，47(8)：87-90,，96.
Channel estimation for 1-bit extremely massive MIMO via deep learning
Pu Xumin1,，2，Wu Chao1,，2,，Yang Xiaolong1，2
1.School of Communication and Information Engineering,，Chongqing University of Posts and Telecommunications,， Chongqing 400065，China,； 2.Chongqing Key Laboratory of Mobile Communications Technology,，Chongqing University of Posts and Telecommunications,， Chongqing 400065，China
Abstract： Extremely massive multiple input multiple output(MIMO) has shown considerable potential in future mobile communications. However, the use of extremely massive aperture arrays will lead to spatial non-stationary channel conditions,，and each antenna of the base station is equipped with a high-precision quantizer, the power consumption of the system will be greatly increased, which will hinder the widespread application of ultra-large-scale MIMO systems. Therefore, this article assumes that each antenna of the base station is equipped with a pair of 1-bit analog-to-digital converters(ADC), and uses the mapping relationship between the sub-array and the user to describe the non-stationary channel characteristics. Based on the powerful generalization ability of neural network(DNN), this paper designs a new generative supervised DNN model that can be trained with a reasonable number of pilots. The simulation results show that the proposed network can achieve better estimation performance with less pilots and achieve a good balance between performance and complexity.
Key words : channel estimation,；deep learning；spatial non-stationary,；1-bit ADC
