超大规模MIMO-OTFS系统上行辅助的下行信道估计方法

doi:10.11959/j.issn.2096-3750.2023.00351

物联网学报 ›› 2023, Vol. 7 ›› Issue (4): 28-38.doi: 10.11959/j.issn.2096-3750.2023.00351

• 理论与技术 • 上一篇

超大规模MIMO-OTFS系统上行辅助的下行信道估计方法

蒲旭敏¹^,², 邓凯元², 陈前斌²

¹ 东南大学移动通信国家重点实验室，江苏南京 210096
² 重庆邮电大学通信与信息工程学院，重庆 400065

修回日期:2023-06-19 出版日期:2023-12-01 发布日期:2023-12-01
作者简介:蒲旭敏（1983- ），男，东南大学移动通信国家重点实验室在站博士后，重庆邮电大学副教授，主要研究方向为超大规模MIMO信号处理和信息传输理论等无线通信技术研究
邓凯元（1998- ），男，重庆邮电大学通信与信息工程学院硕士生，主要研究方向为超大规模 MIMO、信道估计在物联网技术中的应用等
陈前斌（1967- ），男，重庆邮电大学教授、博士生导师，主要研究方向为个人通信、多媒体信息处理与传输、下一代移动通信网络等
基金资助:
国家自然科学基金资助项目(61701062);中国博士后科学基金资助项目(2019M651649);江苏省博士后科研基金资助项目(2018K041c);重庆市教育委员会科学技术研究项目(KJQN202100649);重庆市教育委员会科学技术研究项目(KJQN202000612)

Uplink assisted downlink channel estimation method of extra-large scale MIMO-OTFS system

Xumin PU¹^,², Kaiyuan DENG², Qianbin CHEN²

¹ National Mobile Communications Research Laboratory, Southeast University, Nanjing 210096, China
² School of Communication and Information Engineering, Chongqing University of Posts and Telecommunications, Chongqing 400065, China

Revised:2023-06-19 Online:2023-12-01 Published:2023-12-01
Supported by:
The National Natural Science Foundation of China(61701062);The China Postdoctoral Science Foundation(2019M651649);The Jiangsu Planned Projects for Postdoctoral Research Funds(2018K041c);The Science and Technology Research Program of Chongqing Municipal Education Commission(KJQN202100649);The Science and Technology Research Program of Chongqing Municipal Education Commission(KJQN202000612)

摘要/Abstract

摘要：

针对超大规模多输入多输出（MIMO, multiple-input multiple-output）正交时频空间（OTFS, orthogonal time frequency space）系统，提出了一种适用于高速移动场景的低复杂度下行信道估计方法。不同于现有研究，该方法考虑了超大规模MIMO-OTFS系统显著的空间非平稳特性，基于可视路径区域提出了一种低复杂度的增强型稀疏正交匹配追踪算法，利用频分双工（FDD, frequency-division duplex）模式中上下行信道的映射关系实现上行辅助的下行信道估计。仿真结果表明，所提上行辅助的下行信道估计方法能够充分考虑信道非平稳特性，在降低计算复杂度的同时显著提高信道估计性能，并且在高速移动物联网场景下表现良好。

关键词: 物联网, 正交时频空间调制, 超大规模多输入多输出, 频分双工, 信道估计

Abstract:

A low complexity downlink channel estimation method for high-speed mobile scenarios was proposed for the extra-large scale multiple-input multiple-output (MIMO) orthogonal time-frequency space (OTFS) system.Different from the existing studies, the proposed method considers the significant spatial non-stationary characteristics of extra-large scale MIMO-OTFS system.Based on the visible path region, an enhanced sparse orthogonal matching pursuit (OMP) algorithm with low complexity was proposed, uplink-assisted downlink channel estimation was achieved using the mapping relationship between the uplink and downlink channels in the frequency-division duplex (FDD) mode.Simulation results show that the proposed uplink-assisted downlink channel estimation method can accurately represent the non-stationary of the system, and achieve significant improvement in the channel estimation performance while reducing the computational complexity , and still perform well in high-mobility IoT scenarios.

Key words: IoT, orthogonal time frequency space modulation, extra-large scale MIMO, frequency division duplex, channel estimation

中图分类号:

TN929.5

蒲旭敏, 邓凯元, 陈前斌. 超大规模MIMO-OTFS系统上行辅助的下行信道估计方法[J]. 物联网学报, 2023, 7(4): 28-38.

Xumin PU, Kaiyuan DENG, Qianbin CHEN. Uplink assisted downlink channel estimation method of extra-large scale MIMO-OTFS system[J]. Chinese Journal on Internet of Things, 2023, 7(4): 28-38.

图/表 8

图1

图2

图3

表1

算法计算复杂度对比"

所用算法	计算复杂度
LS算法	$O ((γ_{2} (l_{D})_{p})^{3} + (γ_{2} (l_{D})_{p})^{2} (2 (γ_{1} (n_{D})_{p} - 1)$
	$+ 1 + 2 γ_{3} M / S) + 2 γ_{3} M /$
	$S γ_{2} (l_{D})_{p} (γ_{1} (n_{D})_{p} - 1) + γ_{2} (l_{D})_{p})$
传统OMP算法	$O (P (l_{D})_{p} (n_{D})_{p} γ_{1} N_{D} γ_{2} L_{D} γ_{3} M)$
增强型稀疏OMP算法	$O (L_{s} (l_{D})_{p} (n_{D})_{p} γ_{1} N_{D} γ_{2} L_{D} γ_{3} M / S)$

表1

表2

图4

图5

图6

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信道参数	数值
天线数目M	512
时延域网格维度L _D	512
多普勒域网格维度N _D	128
载波频率 f_c/GHz	5
采样周期T_s/µs	1/20
子载波间隔Δf /kHz	75
上下行频差ΔF/MHz	60
方位角θ_k	[π2,π2)
子阵列数目S	16

超大规模MIMO-OTFS系统上行辅助的下行信道估计方法

Uplink assisted downlink channel estimation method of extra-large scale MIMO-OTFS system

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