Compressive Sensing-Based Adaptive Active User Detection and Channel Estimation: Massive Access Meets Massive MIMO

This paper considers massive access in massive multiple-input multiple-output (MIMO) systems and proposes an adaptive active user detection and channel estimation scheme based on compressive sensing. By exploiting the sporadic traffic of massive connected user equipments and the virtual angular doma...

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Veröffentlicht in:IEEE transactions on signal processing Jg. 68; S. 764 - 779
Hauptverfasser: Ke, Malong, Gao, Zhen, Wu, Yongpeng, Gao, Xiqi, Schober, Robert
Format: Journal Article
Sprache:Englisch
Veröffentlicht: New York IEEE 2020
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Schlagworte:
active user detection
Adaptive systems
Algorithms
approximate message passing
Approximation algorithms
Broadband
Channel estimation
Channels
compressive sensing
Computer simulation
Detection
Domains
Evolutionary algorithms
Massive access
Massive MIMO
massive multiple-input multiple-output
Mathematical analysis
Matrix methods
Message passing
MIMO (control systems)
Performance enhancement
Pilots
Pseudorandom
Sensors
Signal processing algorithms
Sparsity
structured sparsity
ISSN:1053-587X, 1941-0476
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Zusammenfassung:This paper considers massive access in massive multiple-input multiple-output (MIMO) systems and proposes an adaptive active user detection and channel estimation scheme based on compressive sensing. By exploiting the sporadic traffic of massive connected user equipments and the virtual angular domain sparsity of massive MIMO channels, the proposed scheme can support massive access with dramatically reduced access latency. Specifically, we design non-orthogonal pseudo-random pilots for uplink broadband massive access, and formulate the active user detection and channel estimation as a generalized multiple measurement vector compressive sensing problem. Furthermore, by leveraging the structured sparsity of the uplink channel matrix, we propose an efficient generalized multiple measurement vector approximate message passing (GMMV-AMP) algorithm to realize joint active user detection and channel estimation based on a spatial domain or an angular domain channel model. To jointly exploit the channel sparsity present in both the spatial and the angular domains for enhanced performance, a Turbo-GMMV-AMP algorithm is developed for detecting the active users and estimating their channels in an alternating manner. Finally, an adaptive access scheme is proposed, which adapts the access latency to guarantee reliable massive access for practical systems with unknown channel sparsity level. Additionally, the state evolution of the proposed GMMV-AMP algorithm is derived to predict its performance. Simulation results demonstrate the superiority of the proposed active user detection and channel estimation schemes compared to several baseline schemes.
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ISSN:1053-587X
1941-0476
DOI:10.1109/TSP.2020.2967175