面向大规模物联网的随机接入：现状、挑战与机遇

doi:10.11959/j.issn.1000-436x.2021098

Abstract

Abstract:

In conventional communication systems, the random access (RA) is a necessary procedure for a user equipment (UE) to establish wireless connection to the network before actual data transmission.Focusing on the massive IoT with large number of nodes, the fundamental characteristics and limitations of conventional grant-based RA were firstly discussed.Then, the grant-free RA, semi-grant-free RA and unsourced RA for massive IoT were addressed in details from multiple perspectives, including their system models, protocol frameworks, algorithm procedures, system performance, related pros and cons, as well as critical challenges and potential opportunities.Finally, some highlights on the related key techniques and common issues in random access for massive IoT were concluded.

Key words: massive Internet of things, random access, grant-free, unsourced random access, non-orthogonal multiple access

CLC Number:

TN929.5

Pingzhi FAN, Li LI, Huan CHEN, Gaofeng CHENG, Linjie YANG, Xiaobo TANG. Random access for massive Internet of things:current status, challenges and opportunities[J]. Journal on Communications, 2021, 42(4): 1-21.

Figures/Tables 26

序列类型	每个子集内序列个数	低相关区内相关值大小	子集个数	可达PRACH数量
ZC	$⌊ \frac{L}{N_{CS}} ⌋$	0	L? 1	$(L - 1) ⌊ \frac{L}{N_{CS}} ⌋$
ZC	$(L - 1) ⌊ \frac{L}{N_{CS}} ⌋$	$\sqrt{L}$	1	$(L - 1) ⌊ \frac{L}{N_{CS}} ⌋$
ZCm	$(L + 1) ⌊ \frac{L}{N_{CS}} ⌋$	$\sqrt{L + 1}$	L? 1	$(L^{2} - 1) ⌊ \frac{L}{N_{CS}} ⌋$
ZCa	$\approx ⌊ \frac{L}{N_{CS}} ⌋$	$\sqrt{L}$	L	$(L^{2} - 1) ⌊ \frac{L}{N_{CS}} ⌋$

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策略	技术特点	文献	主要方法	特性分析
碰撞控制	随机选择PUSCH资源	文献[46]	基站为一个前导序列分配多个上行PUSCH资源，用户随机选择一个PUSCH资源来传输L2/L3连接请求消息	PUSCH 资源随机选择策略能在一定程度上降低上行授权资源碰撞概率
碰撞控制	限制连接请求消息传输	文献[47]	基站根据当前可分配PUSCH资源数，广播消息传输限制因子，以此控制用户发送连接请求消息的数量	未知确切前导序列碰撞程度时，设定限制因子会增大基站侧控制开销，易造成PUSCH资源浪费
资源复用	时域非正交技术	文献[48]	根据用户上行到达时间的不同将其划分为不同群组，每个群组复用同一个PUSCH资源	该方法需要估计用户上行到达时间，且每个资源复用组的生成过程增大了接入能耗
资源复用	空域非正交技术	文献[49]	根据空间位置关系识别发生前导序列碰撞的多个用户，并将它们视为一个NOMA组，复用同一个PUSCH资源	对碰撞用户的识别能力有限，限制了其在大规模随机接入场景中的应用
碰撞解调	分布式导频正交化	文献[50]	将L2/L3连接请求消息中包含的参考导频信号正交化，并采用多天线技术对多个L2/L3连接请求消息进行解调	正交参考导频信号个数有限，不足以支撑大量用户碰撞情况
碰撞解调	子帧间串行干扰消除（SIC, successive interference cancellation）技术	文献[51]	在基础侧部署缓存器，用于存储发生碰撞的L2/L3连接请求消息，再利用子帧间SIC技术进行解调	所提子帧间迭代式SIC技术仅适用于少量用户碰撞的情况

所用序列类型	所满足评估准则	所达重构精度	所需硬件条件
Gaussian 随机序列	RIP准则	较高，设为P_correct	高
Bernoulli 随机序列	RIP准则	≈P_correct	低
Kasami序列^[65]	STRIP准则	>P _correct	低
Udaya-Siddiq序列^[66]	相干性准则	>P _correct	低
Rademacher 序列	Spark下界	>Pcorrect	低

重构算法	计算复杂度
BP	O(N ³)
IT	O(N ²)
LASSO	O(NL logN)
MP	O(NlogN)
OMP	O (KNL)
MAP-CoSaMP	O (NL)
AMP	O (NL)

方案名称	配对过程	优缺点
开环方案	1) 基站广播信道增益门限值τ，免授权用户将自身上行信道增益与门限值比较2) 场景1下，信道增益低于τ的任意免授权用户可与授权用户构成NOMA组；场景2下，信道增益高于τ的任意免授权用户可参与构成NOMA组	优点：控制开销低，允许大量免授权用户复用信道资源
开环方案		缺点：复用系统资源的免授权用户数随机波动，数量大时导致多用户检测复杂度极高，传输性能下降
分布式竞争方案	1) 基于开环方案确定可配对的免授权用户范围2) 开启竞争时间窗口，免授权用户根据自身上行信道增益获取回退值τ_m 3) 场景1下，自身信道增益最差的免授权用户可与授权用户构成NOMA组；场景2下，自身信道增益最好的免授权用户可参与构成NOMA组	优点：复用信道资源的免授权用户数量可控，降低多用户检测算法复杂度，保证传输性能
分布式竞争方案		缺点：控制复杂，网络中共存用户总数下降

方案名称	应用场景	典型接入性能	优缺点
无协调免授权	完成大规模随机接入场景中活跃用户的数据传输	使用信道资源个数为30 000，信噪比为3 dB，活跃用户个数为200时，误码率可达0.05	优点：支持总用户数量大，不需要显式的活跃用户身份检测环节，适用于低时延传输场景
无协调免授权	完成大规模随机接入场景中活跃用户的数据传输	使用信道资源个数为30 000，信噪比为3 dB，活跃用户个数为200时，误码率可达0.05	缺点：无前导序列，信道估计困难，译码器复杂度较高
基于压缩感知的免授权^[74-77]	完成大规模随机接入场景中活跃用户身份检测与信道估计	总用户个数为4 000，前导序列长度为800，信噪比为3 dB,活跃用户个数为200时，误码率可达0.012	优点：联合完成活跃用户身份检测与信道估计，天线个数充分多时，检测误差可迫近零
基于压缩感知的免授权^[74-77]	完成大规模随机接入场景中活跃用户身份检测与信道估计	总用户个数为4 000，前导序列长度为800，信噪比为3 dB,活跃用户个数为200时，误码率可达0.012	缺点：前导序列开销较大，支持总用户数受前导序列长度制约

Random access for massive Internet of things:current status, challenges and opportunities

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