Channel Estimation With Iterative Hard Thresholding in mMTC Communications

In the context of Internet of Things (IoT) applications, the field of massive Machine-Type Communications (mMTC) has experienced rapid development, but still faces specific challenges such as low latency and high reliability for massive device connectivity and channel estimation (CE). We propose two...

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Vydáno v:IEEE transactions on vehicular technology s. 1 - 6
Hlavní autoři: Zhang, Xiaoxu, Meng, Yuchao, Karagiannidis, George K., Ma, Zheng, Liu, Gang, Yang, Boran
Médium: Journal Article
Jazyk:angličtina
Vydáno: IEEE 2025
Témata:
Accuracy
Channel estimation
Convergence
Estimation
Fast Iterative Hard Thresholding
Internet of Things
Iterative algorithms
Iterative Hard Thresholding
Machine-Type Communications
NOMA
Sensors
Sparse Channel Estimation
Sparse matrices
Vectors
ISSN:0018-9545, 1939-9359
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Shrnutí:In the context of Internet of Things (IoT) applications, the field of massive Machine-Type Communications (mMTC) has experienced rapid development, but still faces specific challenges such as low latency and high reliability for massive device connectivity and channel estimation (CE). We propose two Iterative Hard Thresholding algorithms for channel estimation in Grant-Free Non-Orthogonal Multiple Access (GF-NOMA) systems, including Normalized Iterative Hard Thresholding (NIHT) and Fast Iterative Hard Thresholding (FIHT) method. As a sparse gradient descent algorithm, NIHT provides good performance in CE, but its slow convergence limits efficiency. By introducing momentum iteration and acceleration step strategies, FIHT significantly enhances convergence speed and reduces computational complexity. Through simulation testing, FIHT demonstrates superior performance in complex environments, outperforming existing algorithms in both efficiency and effectiveness.
ISSN:0018-9545
1939-9359
DOI:10.1109/TVT.2025.3633486