频偏下基于张量分解的联合设备活跃性检测和信道估计

渠瑞芸; 刘祖军; 黄蓓蕾

doi:10.11959/j.issn.2096-3750.2025.00424

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频偏下基于张量分解的联合设备活跃性检测和信道估计

理论与技术 | 更新时间：2025-07-29

- 频偏下基于张量分解的联合设备活跃性检测和信道估计
- Tensor decomposition based-joint active device detection and channel estimation under frequency offset
- 物联网学报 2025年9卷第2期页码：152-160
- 作者机构：
  
  西安电子科技大学通信工程学院，陕西西安 710071
- 作者简介：
  
  [ "渠瑞芸（1999‒ ），女，西安电子科技大学通信工程学院硕士生，主要研究方向为新一代移动通信、物联网技术等。" ]
  [ "刘祖军（1976‒ ），男，西安电子科技大学通信工程学院教授、博士生导师，主要研究方向为新一代移动通信、物联网技术、智能通信信号处理等。" ]
  [ "黄蓓蕾（1998‒ ），女，西安电子科技大学通信工程学院博士生，主要研究方向为新一代移动通信、物联网技术等。" ]
- 基金信息：
  
  国家自然科学基金资助项目(62171345)
- DOI：10.11959/j.issn.2096-3750.2025.00424
  中图分类号： TN926.1
- 收稿日期：2024-11-06，
  
  修回日期：2025-05-22，
  
  纸质出版日期：2025-06-10
- 稿件说明：
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渠瑞芸,刘祖军,黄蓓蕾.频偏下基于张量分解的联合设备活跃性检测和信道估计[J].物联网学报,2025,09(02):152-160.

QU Ruiyun,LIU Zujun,HUANG Beilei.Tensor decomposition based-joint active device detection and channel estimation under frequency offset[J].Chinese Journal on Internet of Things,2025,09(02):152-160.
渠瑞芸,刘祖军,黄蓓蕾.频偏下基于张量分解的联合设备活跃性检测和信道估计[J].物联网学报,2025,09(02):152-160. DOI： 10.11959/j.issn.2096-3750.2025.00424.

QU Ruiyun,LIU Zujun,HUANG Beilei.Tensor decomposition based-joint active device detection and channel estimation under frequency offset[J].Chinese Journal on Internet of Things,2025,09(02):152-160. DOI： 10.11959/j.issn.2096-3750.2025.00424.

摘要

在未来无线蜂窝网络中，支持物联网（IoT

Internet of things）和机器类通信（MTC

machine type communication）的大规模接入成为关键性任务。为减少设备在接入时产生的碰撞和信令开销，研究人员提出了免授权随机接入（GF-RA

grant-free random access）方案。在GF-RA中，核心任务是联合设备活跃性检测和信道估计（JADCE

joint active device detection and channel estimation）。在实际场景中，低成本的IoT设备通常会使用廉价的晶体振荡器来降低生产成本，产生的频率偏移严重影响了检测性能。而设备的零星活动模式使该联合检测问题可建模为一个大规模稀疏性约束问题。为避免频偏与信道的非线性耦合引入非凸性，首先，利用张量分解将接收信号从前导序列、信道和频偏的三维张量的角度进行建模，随后，利用交替最小二乘（ALS

alternate least square）方法对分解的子问题进行并行求解，可同时获得设备活跃性、信道响应和频偏的估计值。同时，为使子问题变得严格凸，采用近端最小化（PM

proximal minimization）方法加入正则化约束，提高算法的收敛性和稳定性。最后，从天线数和前导序列长度两方面对所提算法的检测性能进行评估。仿真结果表明，该算法在给定的天线数量和前导序列长度变化范围内，漏检概率检测性能接近1.0×10

-3

，信道估计归一化均方误差（NMSE

normalized mean square error）接近1.0×10

-6

。与现有频偏下设备活跃性检测和信道估计的算法对比，该算法在检测性能上有明显提升。

Abstract

In the future wireless cellular networks

the massive access supporting of the Internet of things (IoT) and machine type communication (MTC) has gradually become a pivotal requirement. To reduce collisions and signaling overhead generated by devices during access

grant-free random access (GF-RA) method has been proposed. In GF-RA

the critical task is the joint active device detection and channel estimation (JADCE). However

in practical scenarios

low-cost IoT devices are usually equipped with inexpensive crystal oscillators to reduce costs

thus the frequency offsets are inevitable and seriously degrade the JADCE performance. The sporadic activity pattern of the devices enables the JADCE to be formulated as a large-scale sparsity constraint problem. In order to avoid the non-convexity introduced by the nonlinear between the frequency offsets and the channels

firstly

tensor decomposition was used to model the received signal fro

m the perspective of the preamble sequence

channel

and frequency offset. Then

the alternating least square (ALS) method was exploited to solve the decomposed subproblems in parallel

and the estimated values of device activities

channel response and frequency offsets could be obtained simultaneously. Moreover

in order to make the subproblem strictly convex

the proximal minimization (PM) method was used to add the regularization constraints

which improved the convergence and stability of the proposed JADCE algorithm. Finally

the detection performance of the proposed algorithm was evaluated based on the number of antennas and the length of the preamble sequence. The simulation results show that the proposed JADCE algorithm achieves a missed detection probability close to 1.0×10

-3

within the given range of antenna numbers and the preamble sequence length variation

and approaches the normalized mean square error (NMSE) of channel estimation to 1.0×10

-6

. Compared with the existing algorithms under the frequency offsets

the proposed algorithm has a significant improvement in detection performance.

关键词

Keywords

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