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On-line Access: 2024-08-27

Received: 2023-10-17

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Frontiers of Information Technology & Electronic Engineering 

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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):  Beijing University of Posts and Telecommunications; 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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Lijun ZHU, Kaihui LIU, Liangtian WAN, Lu SUN, Yifeng XIONG. Joint active user detection and channel estimation for massive machine-type communications: a difference-of-convex optimization perspective[J]. Frontiers of Information Technology & Electronic Engineering,in press.https://doi.org/10.1631/FITEE.2400035

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author="Lijun ZHU, Kaihui LIU, Liangtian WAN, Lu SUN, Yifeng XIONG",
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year="in press",
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doi="https://doi.org/10.1631/FITEE.2400035"
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%T Joint active user detection and channel estimation for massive machine-type communications: a difference-of-convex optimization perspective
%A Lijun ZHU
%A Kaihui LIU
%A Liangtian WAN
%A Lu SUN
%A Yifeng XIONG
%J Frontiers of Information Technology & Electronic Engineering
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%I Zhejiang University Press & Springer
doi="https://doi.org/10.1631/FITEE.2400035"

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A1 - Lu SUN
A1 - Yifeng XIONG
J0 - Frontiers of Information Technology & Electronic Engineering
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Abstract: 
Sparse-based joint active user detection and channel estimation (JADCE) algorithms are crucial in grantfree 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, the existing sparse-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 first formulate JADCE as a joint joint-sparse signal recovery problem, leveraging the block-type row-sparse structure of 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, the simulation results demonstrate that the two proposed difference-of-convex (DC) algorithms achieve effective active user detection and accurate channel estimation compared with the state-of-the-art compressed sensing-based JADCE techniques.

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