基于分组导频与压缩感知的大规模随机接入方案

俞龙鸣; 吴烁雨; 谌翔; 张晶

doi:null

您当前的位置：

首页 >

文章列表页 >

基于分组导频与压缩感知的大规模随机接入方案

更新时间：2026-03-03

- 基于分组导频与压缩感知的大规模随机接入方案
- A Massive Access Scheme Based on Group Preamble and Compressive Sensing
- 物联网学报 2026年
- 作者机构：
  
  1.南京邮电大学波特兰学院，江苏南京 210023
  2.南京邮电大学通信与信息工程学院，江苏南京 210023
  3.江苏省无线通信与物联网重点实验室，江苏南京 210003
- 作者简介：
  
  张晶，jingzhang@njupt.edu.cn
- 基金信息：
  
  国家重点研发计划(2020YFB1807202);国家自然科学基金资助项目;The National Natural Science Foundation of China(92067201);江苏省重点研发计划;The Key Research and Development Program of Jiangsu Province(BE2020084-1)
- DOI：
  中图分类号： TN929.5
- 收稿：2025-11-01，
  
  修回：2026-03-01，
  
  录用：2026-03-03，
- 稿件说明：
移动端阅览
俞龙鸣, 吴烁雨, 谌翔, 等. 基于分组导频与压缩感知的大规模随机接入方案[J/OL]. 物联网学报, 2026.

YU Long-Ming, WU Shuo-Yu, CHEN Xiang, et al. A Massive Access Scheme Based on Group Preamble and Compressive Sensing[J/OL]. Chinese Journal on Internet of Things, 2026.
俞龙鸣, 吴烁雨, 谌翔, 等. 基于分组导频与压缩感知的大规模随机接入方案[J/OL]. 物联网学报, 2026. DOI：

YU Long-Ming, WU Shuo-Yu, CHEN Xiang, et al. A Massive Access Scheme Based on Group Preamble and Compressive Sensing[J/OL]. Chinese Journal on Internet of Things, 2026. DOI：

摘要

针对大规模物联网终端零星随机接入传输难题，提出一种基于分组导频和压缩感知（Compressive Sensing

CS）的大规模接入方案。首先，基站对用户预分组并为每组用户配置一个公共导频，用户采用导频、ID和数据构成的组合帧执行上行随机接入传输。然后，基站逐帧接收上行信号，依次建立导频时隙、ID时隙、数据时隙的上行接收信号模型。最后，设计了基于用户分组的同步正交匹配追踪（Group Based Synchronized Orthogonal Matching Pursuit，GB-SOMP）算法，先利用导频时隙接收信号检测活跃分组，再利用数据时隙的接收信号精确估计活跃用户集合，进而完成活跃用户上行信道系数估计并重构上行数据。仿真结果表明，GB-SOMP算法的时间复杂度和检测精度均优于经典SOMP算法；所提随机接入方案有效提升了无线网络的接入承载能力。

Abstract

For the sporadic random access transmission scenario of massive IoT devices

a massive access scheme based on grouped pilots and compressive sensing (CS) is proposed. First

the base station pre-groups users and assigns a common preamble to each group

while users perform uplink random access transmission using a combined frame consisting of pilot

and data. Then

the base station receives uplink signals frame by frame and sequentially establishes the uplink signal models for the preamble slot

ID slot

and data slot. Finally

a group-based synchronized orthogonal matching pursuit (GB-SOMP) algorithm is designed. It first detects active groups using the preamble slot received signals

then accurately estimates the active user set with the data-slot received signals

and subsequently completes the estimation of uplink channel coefficients for active users and reconstructs the uplink data. Simulation results show that the GB-SOMP algorithm outperforms typical SOMP algorithms in both computational complexity and detection accuracy. The proposed random access scheme effectively enhances the access capacity of wireless networks.

关键词

Keywords

references

GAO Z , KE M L , MEI Y K , et al . Compressive-sensing-based grant-free massive access for 6G massive communication [J ] . IEEE Internet of Things Journal , 2024 , 11 ( 5 ): 7411 - 7435 .

MA Z , WU W , GAO F , et al . Model-driven deep learning for non-coherent massive machine-type communications [J ] . IEEE Transactions on Wireless Communications , 2024 , 23 ( 3 ): 2197 – 2211 .

QIAO L , GAO Z , HE C , et al . Non-coherent grant-free massive IoT access for mMTC: an index modulation perspective [C ] // Proceedings of the 2021 IEEE/CIC International Conference on Communications in China (ICCC) . Piscataway : IEEE Press , 2021 : 694 - 699 .

SHAHAB M B , ABBAS R , SHIRVANIMOGHADDAM M , et al . Grant-free non-orthogonal multiple access for IoT: a survey [J ] . IEEE Communications Surveys & Tutorials , 2020 , 22 ( 3 ): 1805 - 1838 .

BOCKELMANN C , SCHEPKER H F , DEKORSY A . Compressive sensing based multi-user detection for machine-to-machine communication [J ] . Transactions on Emerging Telecommunications Technologies , 2013 , 24 ( 4 ): 389 - 400 ．

DETERME J F , LOUVEAUX J , JACQUES L , et al . On the exact recovery condition of simultaneous orthogonal matching pursuit [J ] . IEEE Signal Processing Letters , 2016 , 23 ( 1 ): 164 - 168 .

KE M L , GAO Z , WU Y P , et al . Compressive massive random access for massive machine-type communications (mMTC) [C ] // Proceedings of the 2018 IEEE Global Conference on Signal and Information Processing (GlobalSIP) . Piscataway : IEEE Press , 2018 : 156 - 160 .

DETERME J F , LOUVEAUX J , JACQUES L , et al . On the noise robustness of simultaneous orthogonal matching pursuit [J ] . IEEE Transactions on Signal Processing , 2017 , 65 ( 4 ): 864 - 875 .

ZHANG W , KIM T . Successful recovery performance guarantees of SOMP under the ℓ₂-norm of noise [J ] . IEEE Transactions on Vehicular Technology , 2024 , 73 ( 2 ): 2156 - 2170 .

LIU L , YU W . Massive connectivity with massive MIMO: Part I: Device activity detection and channel estimation [J ] . IEEE Transactions on Signal Processing , 2018 , 66 ( 11 ): 2933 - 2946 ．

HARA T , ISHIBASHI K . Blind multiple measurement vector AMP based on expectation maximization for grant-free NOMA [J ] . IEEE Wireless Communications Letters , 2022 , 11 ( 6 ): 1201 - 1205 .

BAI Y , CHEN W , AI B , et al ． Contention based massive access scheme for B5 G: a compressive sensing method[C ] // Proceedings of 2020 International Wireless Communications and Mobile Computing (IWCMC) ． Piscataway : IEEE Press ， 2020 ： 1854 - 1859 ．

MEI Y K , GAO Z , MI D , et al . Compressive sensing based grant-free random access for massive MTC [C ] // Proceedings of the 2020 International Conference on UK-China Emerging Technologies (UCET) . Piscataway : IEEE Press , 2020 : 1 - 4 .

DU Y , DONG B H , ZHU W Y , et al . Joint channel estimation and multiuser detection for uplink grant-free NOMA [J ] . IEEE Wireless Communications Letters , 2018 , 7 ( 4 ): 682 - 685 ．

Du Y , Dong B H , Zhu W Y , et al . Joint channel estimation and multiuser detection for uplink grant-free NOMA [J ] . IEEE Wireless Communications Letters , 2018 , 7 ( 4 ): 682 - 685 . [LinkOut ]

KE M L , GAO Z , WU Y P , et al . Compressive sensing-based adaptive active user detection and channel estimation: Massive access meets massive MIMO [J ] . IEEE Transactions on Signal Processing , 2020 , 68 : 764 - 779 ．

LIU K H , LI X N , ZHAO H Y , et al ． Joint active user detection and channel estimation for massive grant-free access via difference of convex programming [C ] // Proceedings of the GLOBECOM 2023 - 2023 IEEE Global Communications Conference . Piscataway : IEEE Press , 2023 : 2335 - 2340 .

BAI Y , CHEN W , SUN F F , et al . Data-driven compressed sensing for massive wireless access [J ] . IEEE Communications Magazine , 2022 , 60 ( 11 ): 28 - 34 ．

DU Y , CHENG C , DONG B H , et al ． Block-compressed-sensing-based multiuser detection for uplink grant-free NOMA systems [C ] // Proceedings of the 2018 IEEE International Conference on Communications (ICC) . Piscataway : IEEE Press , 2018 : 1 - 7 .

GAO Z , DAI L L , WANG Z C , et al . Spatially common sparsity based adaptive channel estimation and feedback for FDD massive MIMO [J ] . IEEE Transactions on Signal Processing , 2015 , 63 ( 23 ): 6169 - 6183 . [LinkOut ]

GAO P Y , LIU Z L , XIAO P , et al . Low-complexity channel estimation and multi-user detection for uplink grant-free NOMA systems [J ] . IEEE Wireless Communications Letters , 2022 , 11 ( 2 ): 263 - 267 .

CHANG J Y , FU X J , ZHAN K , et al . Target detection method based on adaptive step-size SAMP combining off-grid correction for coherent frequency-agile radar [J ] . Remote Sensing , 2023 , 15 ( 20 ): 4921 .

LIU Z K , YU S W . Alternating direction method of multipliers based on ℓ2,0-norm for multiple measurement vector problem [J ] . IEEE Transactions on Signal Processing , 2023 , 71 : 3490 - 3501 .

BAI Y N , CHEN W , AI B , et al . Contention-based nonorthogonal massive access with massive MIMO [J ] . China Communications , 2020 , 17 ( 11 ): 79 - 90 ．

BAI Y N , CHEN W , AI B , et al . Deep learning for asynchronous massive access with data frame length diversity [J ] . IEEE Transactions on Wireless Communications , 2024 , 23 ( 6 ): 5529 - 5540 .

RUIZ‑GUIROLA D E , LÓPEZ O L A , MONTEJO‑SANCHEZ S . Modeling IoT traffic patterns: Insights from a statistical analysis of an MTC dataset [J ] . Expert Systems with Applications , 2025 , 272 : 126726 ．

WANG Q P , LIU L , ZHANG S W , et al . On massive IoT connectivity with temporally-correlated user activity [C ] // Proceedings of the 2021 IEEE International Symposium on Information Theory (ISIT) . Piscataway : IEEE Press , 2021 : 3020 - 3025 .

BOISGUENE R , ALTHAMARY I , HUANG C W . A Learning-Based Energy-Efficient Device Grouping Mechanism for Massive Machine-Type Communication in the Context of Beyond 5G Networks [J ] . Journal of Sensor and Actuator Networks , 2024 , 13 ( 3 ): 33 .

YANG B , WEI F , SHE X , et al . Intelligent random access for massive-machine type communications in sliced mobile networks [J ] . Electronics , 2023 , 12 ( 2 ): 329 .

SHCHUKINA A , KASPRZAK P , DASS R , et al . Pitfalls in compressed sensing reconstruction and how to avoid them [J ] . Journal of Biomolecular NMR , 2017 , 68 ( 2 ): 79 - 98 .

FOUCART S , RAUHUT H . A Mathematical Introduction to Compressive Sensing [M ] . New York, NY : Springer New York , 2013 : 93 – 105 .

浏览量

下载量

CSCD

文章被引用时，请邮件提醒。

提交

工具集

关联资源

基于轻量并行去噪网络的OTFS信道估计算法

频偏下基于张量分解的联合设备活跃性检测和信道估计

环境物联网中的信道估计

基于张量分解和流形优化的双IRS辅助毫米波MIMO系统信道估计

面向6G的生成对抗网络研究进展综述