基于深度學(xué)習(xí)的1-比特超大規(guī)模MIMO信道估計(jì)
2021年電子技術(shù)應(yīng)用第8期
蒲旭敏1,2,吳 超1,2,楊小瓏1,2
1.重慶郵電大學(xué) 通信與信息工程學(xué)院,重慶400065;2.重慶郵電大學(xué) 移動(dòng)通信技術(shù)重慶市重點(diǎn)實(shí)驗(yàn)室,重慶400065
摘要: 超大規(guī)模多輸入多輸出(Multiple Input Multiple Output,MIMO)技術(shù)在未來的移動(dòng)通信中具有巨大的潛力。然而,采用超大型的天線陣列會(huì)出現(xiàn)空間非平穩(wěn)性信道特征,如果為基站的每根天線都配備一個(gè)高精度量化器,系統(tǒng)功耗將大幅度增加,從而阻礙超大規(guī)模MIMO系統(tǒng)的廣泛應(yīng)用。因此,假設(shè)基站的每根天線都配備有一對(duì)1-比特模數(shù)轉(zhuǎn)換器(Analog-to-Digital Converters,ADC),利用子陣列與用戶之間的映射關(guān)系來描述非平穩(wěn)信道特征,借助深度神經(jīng)網(wǎng)絡(luò)(Deep Neural Network,DNN)強(qiáng)大的泛化能力,設(shè)計(jì)一種新的生成式監(jiān)督DNN模型,該模型可以使用合理數(shù)量的導(dǎo)頻進(jìn)行訓(xùn)練。仿真結(jié)果表明,所提出的網(wǎng)絡(luò)可以使用較少的導(dǎo)頻得到較好的估計(jì)性能,在性能與計(jì)算復(fù)雜度之間取得良好的平衡。
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.
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
0 引言
大規(guī)模多輸入多輸出(Multiple Input Multiple Output,MIMO)技術(shù)是第五代(Fifth Generation,5G)移動(dòng)通信的關(guān)鍵技術(shù)之一。隨著天線陣列尺寸數(shù)量級(jí)的增加,形成了超大規(guī)模MIMO系統(tǒng)。通過增加天線陣列的尺寸,可以提升頻譜效率、能量效率和空間分辨率等,還有望獲取超高的數(shù)據(jù)速率和系統(tǒng)吞吐量。超大規(guī)模MIMO技術(shù)也因此成為第六代(Sixth Generation,6G)移動(dòng)通信關(guān)鍵技術(shù)的候選[1]。然而,大口徑陣列的使用會(huì)造成不同的信道條件。當(dāng)整個(gè)陣列的孔徑有限并且服務(wù)相同的用戶時(shí),空間穩(wěn)定性是成立的。但是,對(duì)于大孔徑陣列,由于天線陣列不同區(qū)域所服務(wù)的用戶不同,接收功率的級(jí)別也因此不同,這稱為空間非平穩(wěn)性[2]。因此,可以引入子陣列和用戶可見區(qū)域(Visibility Region,VR)來描述信道非平穩(wěn)性[3]。
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作者信息:
蒲旭敏1,2,吳 超1,2,楊小瓏1,2
(1.重慶郵電大學(xué) 通信與信息工程學(xué)院,重慶400065;2.重慶郵電大學(xué) 移動(dòng)通信技術(shù)重慶市重點(diǎn)實(shí)驗(yàn)室,重慶400065)
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