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Joint active user detection and channel estimation for massive machine-type communications: a difference-of-convex optimization perspective
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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, 1998, -1(-1): .
@article{title="Joint active user detection and channel estimation for massive machine-type communications: a difference-of-convex optimization perspective",
author="Lijun ZHU, Kaihui LIU, Liangtian WAN, Lu SUN, Yifeng XIONG",
journal="Frontiers of Information Technology & Electronic Engineering",
volume="-1",
number="-1",
pages="",
year="1998",
publisher="Zhejiang University Press & Springer",
doi="10.1631/FITEE.2400035"
}
%0 Journal Article
%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 Journal of Zhejiang University SCIENCE C
%V -1
%N -1
%P
%@ 2095-9184
%D 1998
%I Zhejiang University Press & Springer
%DOI 10.1631/FITEE.2400035
TY - JOUR
T1 - Joint active user detection and channel estimation for massive machine-type communications: a difference-of-convex optimization perspective
A1 - Lijun ZHU
A1 - Kaihui LIU
A1 - Liangtian WAN
A1 - Lu SUN
A1 - Yifeng XIONG
J0 - Journal of Zhejiang University Science C
VL - -1
IS - -1
SP -
EP -
%@ 2095-9184
Y1 - 1998
PB - Zhejiang University Press & Springer
ER -
DOI - 10.1631/FITEE.2400035
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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