6G无线通信物理层关键技术

doi:10.11959/j.issn.1000-0801.2023250

Abstract

Abstract:

6G technologies are expected to be deployed commercially around 2030.It is expected that 6G will provide performance superior to 5G in the key performance indicators, such as traffic density, connection density, and energy efficiency.In order to meet the personalized and diversified requirements of vertical industries in 2030 and beyond, 6G will adopt many new technologies, such as new network architecture, new physical layer wireless transmission technologies, and new security sustainability technologies.Starting from the potential key technologies in physical layer of 6G, terahertz (THz), ultra-large-scale multiple-input multiple-output (MIMO), intelligent reflecting surface (IRS), and physical security were introduced mainly.In addition, the challenges of potential key technologies of 6G were briefly introduced, which were expected to provide some reference for future 6G physical layer-related research.

Key words: 6G, THz, ultra-large-scale MIMO, IRS, challenge

CLC Number:

TN929.5

Shanjin NI, Liang SHEN, Shan NING, Xin WAN. Key technologies in physical layer of 6G wireless communications[J]. Telecommunications Science, 2023, 39(12): 1-18.

Figures/Tables 13

研究方向	当前主要解决方案
无密钥物理层	文献[65]在莱斯信道基础上提出一种线性预编码器设计方案来进行数据和人工噪声传输，以增强大规模 MIMO 无人驾驶飞行器通信系统的安全性
安全传输技术	文献[66]探究了如何利用 THz 通信固有的多路径特性来降低消息窃听概率，同时提出在通信实体间共享当前可用的多个 THz 传播路径上的数据传输，虽然以牺牲部分链路容量为代价，但这种方式即使在多个窃听者协作窃听时仍然可以有效降低消息窃听概率
	文献[67]面向人工噪声辅助的安全MIMO-IRS无线通信系统，提出在发射功率限制和IRS相移单位模数的约束下，联合优化基站处的发射预编码矩阵、人工噪声的协方差矩阵和IRS处的相移，以最大化保密速率
	文献[68]利用射频指纹的唯一性与第三方不可复制性，提出一种基于射频指纹的接收信号来源安全识别方案，从而实现了信息保护和设备的快速认证
基于信道的物理层安全密钥生成技术	文献[69]对6G无线通信内生安全的理念与构想进行了总结与分析，提出逼近香农“一次一密”的通信安全一体化概念，指出当前基于信道的物理层安全生成技术主要包括：一是利用无线信道的独一无二性、不可复制性等内在安全属性，从无线信道中生成安全密钥，从而保证通信的可靠性^[70]；二是将无线信道天然传播特性与6G新兴使能技术（如THz、超大规模MIMO、IRS等）结合，通过挖掘新兴使能技术带来的信道新特征以及新的传播特性，从数据传输速率及无线密钥生成速率两个方面入手，逼近实现“一次一密”效果^[71-72]；三是将传播信号与无线密钥设计结合，实现面向通信安全一体化的高速密钥生成及数据安全高效传输机制^[73]

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性能指标	6G	5G
峰值速率/（Gbit·s^-1）	＞100	10
用户体验速率/（Gbit·s^-1）	＞10	1
流量密度/（Tbit·(s·km²) ^-1）	＞100	10
连接数密度/（百万·km^-2）	＞10	1
时延	＜1 ms	毫秒级
移动性/（km·h^-1）	＞1 000	350
频谱效率	大于5G的3倍	4G的3～5倍
能量效率	大于5G的10倍	4G的1 000倍
覆盖率	＞99%	70%左右
可靠性	＞99.999%	99.9%左右
定位精度	厘米级	米级
接收机敏感度/dBm	＜-130	-120左右

研究方向	当前主要解决方案
信道建模与信道测量	当前 THz 信道模型主要分为两类：确定性信道模型^[29-30]及统计信道模型^[31-32]。文献[29-30]利用射线追踪技术探究了在视距与非视距路径下的THz信道，并对其进行了简单描述。文献[31-32]根据实验数据，并结合MIMO技术，给出了经典的Saleh-Valenzuela模型
	文献[33]利用射线追踪方法对具体场景的THz信道进行了测量
信道估计	文献[34]通过建立三维坐标对到达角（angle of arrival，AoA）特性进行表征，并利用贝叶斯滤波器对移动用户的AoA进行估计
	文献[35]针对THz移动通信场景，基于THz信道稀疏特性以及移动用户的物理空间特征，利用波束空间技术，对用户的波束空间信道进行信道估计与追踪
覆盖增强	THz信号存在较大的路径损耗以及较差的穿透能力，文献[36]提出利用IRS辅助THz进行通信以扩大THz频段的有效通信范围
	文献[37]提出利用无人驾驶飞行器（unmanned aerial vehicle，UAV）作为空中基站/中继，建立与地面用户的视距连接，从而实现THz通信系统的超可靠低时延通信
调制解调	THz调制解调技术可以助力实现数据在THz频段的远距离传输。THz无线通信的调制方式主要分为直接调制^[38]、太赫兹混频调制和太赫兹光电调制^[17]

研究方向	当前主要解决方案
信道建模	文献[43]对典型城市环境下的超大规模 MIMO 无线信道在波束域的传播特性进行了信道测量实验与数据分析工作，重点分析了超大规模MIMO在波束域的描述以及波束内与波束间的新信道特性
	文献[44-45]对当前文献中与超大规模 MIMO 相关的信道测量、特征分析与建模方法进行综述，并对超大规模MIMO因天线维度增加所表现出来的信道新特性及新模型架构进行了详细阐述
信道估计	文献[46]给出了人工智能技术在6G空口物理层方面的应用，并总结了机器学习技术使能的6G空口物理层大规模MIMO信道估计算法
	文献[47]研究了一种考虑混合场（即远场与近场都考虑）信道模型的实用策略，以实现超大规模MIMO通信系统的有效信道估计，该策略首先提供了一个准则来确定混合场比例，然后根据比例获得相应的信道估计结果
性能分析	自由度（degrees of freedom，DoF）和有效自由度（effective degrees of freedom，EDoF）是测量超大规模MIMO系统性能的重要指标，文献[48]基于二维采样理论给出DoF的近似表达式，文献[49]则给出了EDoF的近似表达式
波束成形/预编码	对于超大规模 MIMO 而言，设计轻量级信号处理方案来降低超大维度信号计算复杂度至关重要。文献[50]提出了一种低计算量的破零预编码方案，该方案运用诺依曼级数展开代替矩阵求逆，为轻量级信号处理设计提供了重要启示
	文献[51]对THz超大规模MIMO系统的相关波束成形技术进行了总结，并提出了基于波束训练的THz频段超大规模MIMO波束成形策略

研究方向	当前主要解决方案
信道建模	文献[52]重点介绍了开源软件SimRIS信道仿真器v2.0版本在模拟阵列响应、元件增益、实际路径损耗和阴影模型等方面的优势，并阐明了IRS在未来无线系统中的潜在用例
	文献[53]利用物理光学技术推导出IRS远场路径损耗，并解释了IRS元件单独充当漫散射体时可以以一定的波束宽度在所需方向上联合波束形成信号的原因
信道估计	文献[54]基于基站-IRS-用户间级联信道的稀疏特性，提出了一种双结构正交匹配追踪算法，从而实现基站-IRS-用户级联信道准确估计
	文献[55]将IRS信道估计问题建模为去噪问题，并采用深度残差学习方法隐式学习残差噪声，以便从基于噪声的接收信号中恢复信道系数
性能分析	文献[56]提出在IRS辅助多用户多输入单输出下行链路通信系统中，通过联合设计基站波束成形与IRS相移矩阵实现所有用户权重和速率最大化
	文献[57]通过联合优化IRS反射系数和MIMO传输协方差矩阵来表征IRS辅助点对点MIMO通信系统的基本容量限制
波束成形/预编码	文献[58]提出在宽带IRS辅助无线通信网络中对基站与IRS的预编码进行联合设计以实现最大化网络容量，并采用改进型交替优化算法来解决预编码联合设计存在的非凸性和高复杂性问题
	文献[59]探究了THz通信场景中大规模MIMO与IRS联合部署下的低成本模拟域数字域混合波束成形方案

研究方向	当前主要解决方案
功率分配的NOMA	文献[81]梳理了功率域非正交多址技术的基本原理，并对当前功率域NOMA的相关研究方向及实际应用中面临的难题进行了归纳讨论
基于交织的NOMA	文献[77]对交织分割多址进行了梳理，并基于密度演化技术提出了一种半解析技术来估计系统的误码率，其可以快速且相对准确地预测交织分割多址方案的性能
	文献[82]提出一种基于坐标交织的低复杂度NOMA方法，该方法通过正确利用两个正交载波，可以同时实现性能增强和连续干扰消除操作数量的减少
基于扰码的NOMA	文献[78]提出了基于扰码的资源扩展多址接入技术，该技术通过为用户分配不同的签名，在同一时间同一频率上发送，以此分离用户
基于扩频的NOMA	文献[79]提出了低复杂度稀疏码多址技术，该技术将比特到正交振幅调制（quadrature amplitude modulation， QAM）符号映射过程和扩频过程组合在一起，并将输入比特直接映射为稀疏码多址码本集的多维码字
图样分割多址接入	文献[80]对基于稀疏扩频的图样分割多址接入进行了详细介绍，通过对发射机和接收机的联合设计，可以实现基于低复杂度的连续干扰消除的多用户检测，有效提高系统性能

Key technologies in physical layer of 6G wireless communications

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