JZUS - Journal of Zhejiang University SCIENCE

Frontiers of Information Technology & Electronic Engineering 2025 Vol.26 No.4 P.588-604

Joint active user detection and channel estimation for massive machine-type communications: a difference-of-convex optimization perspective

Author(s): Lijun ZHU, Kaihui LIU, Liangtian WAN, Lu SUN, Yifeng XIONG
Affiliation(s): School of Information and Communication Engineering, Beijing University of Posts and Telecommunications, Beijing 100876, China; more
Corresponding email(s): kaihuiL@outlook.com
Key Words: Joint active user detection and channel estimation, Massive machine-type communications, Difference-of-convex function algorithm, Alternating direction multiplier method

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Abstract: Sparsity-based joint active user detection and channel estimation (JADCE) algorithms are crucial in grant-free massive machine-type communication (mMTC) systems. The conventional compressed sensing algorithms are tailored for noncoherent communication systems, where the correlation between any two measurements is as minimal as possible. However, existing sparsity-based JADCE approaches may not achieve optimal performance in strongly coherent systems, especially with a small number of pilot subcarriers. To tackle this challenge, we formulate JADCE as a joint sparse signal recovery problem, leveraging the block-type row-sparse structure of millimeter-wave (mmWave) channels in massive multiple-input multiple-output orthogonal frequency division multiplexing (MIMO-OFDM) systems. Then, we propose an efficient difference-of-convex function algorithm (DCA) based JADCE algorithm with multiple measurement vector (MMV) frameworks, promoting the row-sparsity of the channel matrix. To mitigate the computational complexity further, we introduce a fast DCA-based JADCE algorithm via a proximal operator, which allows a low-complexity alternating direction multiplier method (ADMM) to resolve the optimization problem directly. Finally, simulation results demonstrate that the two proposed difference-of-convex (DC) algorithms achieve effective active user detection and accurate channel estimation compared with state-of-the-art compressed sensing based JADCE techniques.

大规模机器类型通信的联合活跃用户检测和信道估计：凸差优化视角

朱丽君¹，刘开晖²，万良田³，孙璐⁴，熊一枫¹
¹北京邮电大学信息与通信工程学院，中国北京市，100876
²东莞理工学院电信工程与智能化学院，中国东莞市，523808
³大连理工大学软件学院，中国大连市，116024
⁴大连海事大学信息科学技术学院，中国大连市，116026
摘要：在免授权的大规模机器类型通信（mMTC）系统中，基于稀疏的联合活跃用户检测和信道估计（JADCE）算法至关重要。传统的压缩感知算法适用于非相干通信系统，其中任意两个测量之间的相关性尽可能小。然而，现有的基于稀疏的JADCE方法在强相干系统中可能无法达到最佳性能，尤其是在少量导频子载波的情况下。为解决这一问题，通过利用大规模多输入多输出正交频分复用（MIMO-OFDM）系统中毫米波信道的块状行稀疏结构，我们将JADCE建模为一个基于多测量向量（MMV）框架的联合稀疏信号恢复问题。然后，从凸差（DC）优化视角提出一种基于凸差算法（DCA）的高效JADCE方法。为进一步降低算法的计算复杂度，引入一种近端算子实现了基于DCA的快速JADCE算法，该算法采用低复杂度的交替方向乘子法（ADMM）直接解决优化问题。仿真实验结果表明，与现有基于压缩感知的JADCE方法相比，本文所提出的两种凸差算法实现了有效的活跃用户检测和精确的信道估计。

关键词：联合活跃用户检测和信道估计；大规模机器类型通信；凸差函数算法；交替方向乘子法

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