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Cluster-based massive access for massive MIMO systems
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AI
DOI：10.23919/JCC.fa.2023-0380.202401.png)
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摘要
En 中文
Massive machine type communication aims to support the connection of massive devices, which is still an important scenario in 6G. In this paper, a novel cluster-based massive access method is proposed for massive multiple input multiple output systems. By exploiting the angular domain characteristics, devices are separated into multiple clusters with a learned cluster-specific dictionary, which enhances the identification of active devices. For detected active devices whose data recovery fails, power domain nonorthogonal multiple access with successive interference cancellation is employed to recover their data via re-transmission. Simulation results show that the proposed scheme and algorithm achieve improved performance on active user detection and data recovery.
Keyword:
Channel estimation
Antennas
Dictionaries
Scattering
Multiuser detection
Clustering algorithms
6G mobile communication
compressive sensing
dictionary learning
multiuser detection
random access
