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Energy-Efficient Cell-Free Massive MIMO with Wireless Fronthaul
KTH, School of Electrical Engineering and Computer Science (EECS), Computer Science, Communication Systems, CoS.ORCID iD: 0000-0002-4640-7020
TOBB University of Economics and Technology, Department of Electrical-Electronics Engineering, Ankara, Turkiye.
KTH, School of Electrical Engineering and Computer Science (EECS), Computer Science, Communication Systems, CoS.ORCID iD: 0000-0002-5954-434X
KTH, School of Electrical Engineering and Computer Science (EECS), Computer Science, Communication Systems, CoS.ORCID iD: 0000-0003-0525-4491
2024 (English)In: Conference Record of the 58th Asilomar Conference on Signals, Systems and Computers, ACSSC 2024, Institute of Electrical and Electronics Engineers (IEEE) , 2024, p. 1591-1596Conference paper, Published paper (Refereed)
Abstract [en]

Cell-free massive MIMO improves the fairness among the user equipments (UEs) in the network by distributing many cooperating access points (APs) around the region while connecting them to a centralized cloud-computing unit that coordinates joint transmission/reception. However, the fiber cable deployment for the fronthaul transport network and activating all available antennas at each AP lead to increased deployment cost and power consumption for fronthaul signaling and processing. To overcome these challenges, in this work, we consider wireless fronthaul connections and propose a joint antenna activation and power allocation algorithm to minimize the end-to-end (from radio to cloud) power while satisfying the quality-of-service requirements of the UEs under wireless fronthaul capacity limitations. The results demonstrate that the proposed methodology of deactivating antennas at each AP reduces the power consumption by 50% and 84% compared to the benchmarks based on shutting down APs and minimizing only the transmit power, respectively.

Place, publisher, year, edition, pages
Institute of Electrical and Electronics Engineers (IEEE) , 2024. p. 1591-1596
National Category
Telecommunications Communication Systems
Identifiers
URN: urn:nbn:se:kth:diva-362686DOI: 10.1109/IEEECONF60004.2024.10942847ISI: 001479671800293Scopus ID: 2-s2.0-105002693497OAI: oai:DiVA.org:kth-362686DiVA, id: diva2:1954128
Conference
58th Asilomar Conference on Signals, Systems and Computers, ACSSC 2024, Hybrid, Pacific Grove, United States of America, Oct 27 2024 - Oct 30 2024
Note

 Part of ISBN 9798350354058

Available from: 2025-04-23 Created: 2025-04-23 Last updated: 2025-12-05Bibliographically approved

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Topal, Ozan AlpBjörnson, EmilCavdar, Cicek

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