大规模MIMO系统中单精度模数转换器的量化门限设置方案

严飞; 李国兵; 张国梅; 吕刚明

doi:10.7652/xjtuxb201608008

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大规模MIMO系统中单精度模数转换器的量化门限设置方案

更新时间：2025-05-08

- 大规模MIMO系统中单精度模数转换器的量化门限设置方案
- Adaptive Quantization Threshold Setting Method for Massive MIMO Systems with 1-bit Analog-to-Digital Converter
- 西安交通大学学报 2016年50卷第8期页码：45-51
- 作者机构：
  
  西安交通大学电子与信息工程学院,西安,710049
- 作者简介：
- 基金信息：
  
  : 2016-02-02。作者简介: 严飞(1989—),男,硕士生;李国兵(通信作者),男,副教授。基金项目: 国家自然科学基金资助项目(61461136001,61401350);国家“863计划”资助项目(2014AA01A707);国家科技重大专项子课题资助项目(2015ZX03001014-003)
- DOI：10.7652/xjtuxb201608008
  中图分类号： TN914
- 网络首发：2016-08-10，
  
  纸质出版：2016
- 稿件说明：
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严飞, 李国兵, 张国梅, 等. 大规模MIMO系统中单精度模数转换器的量化门限设置方案[J]. 西安交通大学学报, 2016,50(8):45-51.

Adaptive Quantization Threshold Setting Method for Massive MIMO Systems with 1-bit Analog-to-Digital Converter[J]. 2016, 50(8): 45-51.
严飞, 李国兵, 张国梅, 等. 大规模MIMO系统中单精度模数转换器的量化门限设置方案[J]. 西安交通大学学报, 2016,50(8):45-51. DOI： 10.7652/xjtuxb201608008.

Adaptive Quantization Threshold Setting Method for Massive MIMO Systems with 1-bit Analog-to-Digital Converter[J]. 2016, 50(8): 45-51. DOI： 10.7652/xjtuxb201608008.

摘要

针对配置单精度模数转换器(ADC)的大规模多输入多输出(MIMO)系统中信道估计误差和上行信号误符号率较高的问题

提出一种基于大规模MIMO信道相关性的自适应ADC量化门限设置方案

并据此给出了相应的信道估计及上行多用户检测方案。首先根据3GPP信道模型建模并生成信道数据

对该数据进行线性分类以获得与发射功率及用户数自适应的量化门限及相应的量化值

以此对上行导频及有用信号进行量化

然后设计基于该自适应量化门限的信道估计及信号检测矩阵

最终检测出上行信号。所提自适应门限设置方法将相关性强的天线进行组合

为同一组合内的多个单精度ADC设置不同的量化门限以提升量化精度。仿真结果表明:与传统固定量化门限的方法相比

所提出的自适应门限设置方法可充分利用信道相关特性

当天线的相关性较强(天线间距小于等于0.5λ)的时候

信道估计的最小均方误差降低到原来的50%左右

同时系统的误符号率提升了5 dB以上。

Abstract

A new adaptive threshold setting method using the channel correlation as well as its corresponding channel estimation and multiuser signal detection scheme is proposed to improve the performance of channel estimation and signal detection in massive multi-input multi-output systems with 1-bit analog-to-digital converter(ADC). Channel data is firstly generated using the 3GPP channel model

and linearly categorized to obtain the quantization thresholds and codebook that are adaptive to the transmitting power and the number of users. Then based on the adaptive thresholds

the channel estimation and signal detection matrices are designed for the final uplink multiuser signal detection using the quantized uplink reference and desired signals. The method combines high-related antennas and jointly sets different thresholds for 1-bit ADCs to improve quantization precision. Simulation results and comparisons with the conventional quantization method with fixed threshold show that the proposed method makes full use of the channel correlation

improves the accuracy of channel estimation by about 50%

and reduces the system symbol error rate by more than 5 dB when the antenna spacing is 0.5λ.

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references

LU L, LI G Y, SWINDLEHURST A L, et al. An overview of massive MIMO: benefits and challenges [J]. IEEE Journal on Selected Topics in Signal Processing, 2014, 8(5): 742-758.

RUSEK F, PERSSON D, BUON K L, et al. Scaling up MIMO: opportunities and challenges with very large arrays [J]. IEEE Magazine in Signal Pro-cessing, 2013, 30(1): 40-60.

PANZNER B, ZIRWAS W, DIERKS S, et al. Deployment and implementation strategies for massive MIMO in 5G [C]∥2014 IEEE Globecom Workshops. Piscataway NJ, USA: IEEE, 2014: 346-351.

JACOBSSON S, DURISI G, COLDREY M, et al. One-bit massive MIMO: channel estimation and high order modulations [C]∥ IEEE International Confe-rence on Communication Workshop. Piscataway, NJ, USA: IEEE, 2015: 1304-1309.

CHIARA R, DANIEL P, ERIK G L. Massive MIM-O with 1-bit ADC [EB/OL]. [2015-08-01]. http:∥arxiv.org/abs/1404.7736.

LIANG N, ZHANG W Y. A mixed-ADC receiver architecture for massive MIMO systems [C]∥2015 IEEE Information Theory Workshop. Piscataway, NJ, USA: IEEE, 2015: 229-233.

ZHANG Ti-Cao, WEN Chao-kai, JIN Shi, et al. Mixed-ADC massive MIMO detectors: performance analysis and design optimization [EB/OL]. [2015-08-01]. http:∥arxiv.org/pdf/1509.07950.pdf.

JUNIL C, JIANHUA M, ROBERT W H. Near maximum-likelihood detector and channel estimator for uplink multiuser massive MIMO systems with one-bit ADCs [EB/OL]. [2015-08-01]. http:∥arxiv.org/abs/1507.04452.

WEN C, WANG C, JIN S, et al. Bayes-optimal joint channel-and-data estimation for massive MIMO with low-precision ADCs [EB/OL]. [2015-08-01]. http:∥arxiv.org/abs/1507.07766.

MO Jianhua,HEATH R W. Limited feedback in multiple-antenna systems with one-bit quantization [C]∥ Conference Record of the 49th Asilomar Conference on Signals, Systems and Computers. Los Alamitos, CA, USA: IEEE Computer Society, 2015: 1432-1436.

JUNIL C, LOVE D J, BROWN D, et al. Quantized distributed reception for MIMO wireless systems using spatial multiplexing [J]. IEEE Transactions on Signal Processing, 2015, 63(8): 3357-3548.

王东明, 张余, 魏浩, 等. 面向5G的大规模天线无线传输理论与技术 [J]. 中国科学: 信息科学, 2016, 46(1): 3-21.

WANG D M, ZHANG Y, WEI H, et al. Large-scale wireless transmission theory and technology in 5G mobile communication [J]. Scientia Sinica: Informationis, 2016, 46(1): 3-21.

KAMMOUN A, KHANFIR H, ALTMAN Z, et al. Preliminary results on 3D channel modeling: from theory to standardization [J]. IEEE Journal on Selected Areas in Communications, 2014, 32(6):1219-1229.

JASSAL A, KHANFIR H, LOPEZ S M. Preliminary system-level simulation results for the 3GPP 3D MIMO channel model [C]∥ 2014 IEEE 80th Vehicular Technology Conference. Piscataway, NJ, USA: IEEE, 2014: 1-5.

THOMAS T A, VOOK F W, MELLIOS E, et al. 3D extension of the 3GPP/ITU channel model [C]∥ 2013 IEEE 77th Vehicular Technology Conference. Piscataway, NJ, USA: IEEE, 2013: 1-5.

MONDAL B, THOMAS T, VISOTSKY E, et al. 3D channel model in 3GPP [J]. IEEE Communications Magazine, 2015, 53(3): 16-23.

朱丰, 张群, 段艳丽, 等. 基于压缩感知和LBG算法的SAR数据压缩与重构方法 [J]. 信号处理, 2012, 28(6): 842-850.

ZHU F, ZHANG Q, DUAN Y L, et al. SAR data compressing and reconstructing method based on compressed sensing and LBG algorithm [J]. Signal Processing, 2012, 28(6): 842-850.

姜高霞, 王文剑. 时序数据曲线排齐的相关性分析方法 [J]. 软件学报, 2014, 25(9): 2002-2017.

JIANG G X, WANG W J. Correlation analysis in curve registration of time series [J]. Journal of Software, 2014, 25(9): 2002-2017.

刘健伶,陈志刚,王磊.一种自适应广义空间调制及其低复杂度算法.2016,50(4):48-53.[doi:10.7652/xjtuxb201604008]

范正光,屈丹,闫红刚,等.借助音频数据的发音字典新词学习方法.2016,50(6):75-82.[doi:10.7652/xjtuxb201606012]

陈斌,胡平舸,屈丹.子空间域相关特征变换与融合的语音识别方法.2016,50(4):60-67.[doi:10.7652/xjtuxb201604010]

王富平,水鹏朗.采用多尺度方向微分比率的角点检测算法.2016,50(4):68-75.[doi:10.7652/xjtuxb201604011]

孙黎,徐洪斌.协作式终端直通系统中星座旋转辅助的干扰避免策略.2015,49(12):6-11.[doi:10.7652/xjtuxb201512 002]

王天荆,姜华,刘国庆.采用分组数据的序贯频谱感知方法.2015,49(12):34-39.[doi:10.7652/xjtuxb201512006]

包昕,周磊砢,何可,等.误码条件下的LDPC码盲识别算法.2015,49(12):53-58.[doi:10.7652/xjtuxb201512009]

张东伟,郭英,齐子森,等.采用空间极化时频分布的跳频信号多参数联合估计算法.2015,49(8):17-23.[doi:10.7652/xjtuxb201508004]

巴斌,郑娜娥,朱世磊,等.利用蒙特卡罗的最大似然时延估计算法.2015,49(8):24-30.[doi:10.7652/xjtuxb201508005]

俱莹,殷勤业,陈媛,等.发射聚焦式的多天线跳空安全通信技术.2015,49(6):22-26.[doi:10.7652/xjtuxb201506004]

刘进,李赞,高锐.低信噪比下采用广义随机共振的能量检测算法.2015,49(6):27-32.[doi:10.7652/xjtuxb201506005]

熊涛,江桦,崔鹏辉,等.应用基扩展模型的混合信号单通道盲分离算法.2015,49(6):60-66.[doi:10.7652/xjtuxb201506 010]

王云龙,吴瑛.联合时延与多普勒频率的直接定位改进算法.2015,49(4):123-129.[doi:10.7652/xjtuxb201504020]

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