Decentralized detection for B5G massive MIMO: When local computation meets iterative algorithm |
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| Institution: | 2. Department of Chemistry and Biochemistry, Arizona State University, Tempe, Arizona;3. Center for Biological Physics, Department of Physics, Arizona State University, Tempe, Arizona;1. The Department of Electrical and Computer Engineering, University of Illinois at Urbana-Champaign, Champaign, IL 61801, USA;2. The Department of Electrical and Electronics Engineering, Bilkent University, Bilkent, Ankara 06800, Turkey;1. Key Laboratory of All Optical Network and Advanced Telecommunication Network of Ministry of Education, Institute of Lightwave Technology, Beijing Jiaotong University, Beijing 100044, China;2. School of Automation, Beijing Information Science and Technology University, Beijing 100192, China;1. Thapar University, Patiala, Punjab, India;2. Radiant Institute of Engineering and Technology, Abohar, Punjab, India |
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| Abstract: | Massive multiple-input multiple-output (MIMO) is a key enabler for 5G and beyond. For signal detection of massive MIMO, computing resources available at the network edge were underexplored in most existing algorithms. For this reason, the paper proposes a new detection algorithm, termed inner-looping decentralized generalized expectation consistent for signal recovery (iDeGEC-SR), which leverages an extra (inner) loop of message passing added to the DeGEC-SR and makes better exploration of the local computing resources. As demonstrated by theoretical analysis and Monte Carlo simulations, the algorithm outperforms state-of-the-art techniques like GEC-SR (in terms of computational complexity), GAMP and DeGEC-SR (in terms of estimation accuracy), considerably. |
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| Keywords: | Massive MIMO Decentralized algorithm Message passing Mean square error |
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