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Physical Layer Abstraction Model for RadioWeaves
Linköping University, Department of Electrical Engineering, Communication Systems. Linköping University, Faculty of Science & Engineering.
Linköping University, Department of Electrical Engineering, Communication Systems. Linköping University, Faculty of Science & Engineering.ORCID iD: 0000-0001-9504-3975
Linköping University, Department of Electrical Engineering, Communication Systems. Linköping University, Faculty of Science & Engineering.
Linköping University, Department of Electrical Engineering, Communication Systems. Linköping University, Faculty of Science & Engineering.ORCID iD: 0000-0002-7599-4367
2022 (English)In: 2022 IEEE 95TH VEHICULAR TECHNOLOGY CONFERENCE (VTC2022-SPRING), IEEE , 2022Conference paper, Published paper (Refereed)
Abstract [en]

RadioWeaves, in which distributed antennas with integrated radio and compute resources serve a large number of users, is envisioned to provide high data rates in next-generation wireless systems. In this paper, we develop a physical layer abstraction model to evaluate the performance of different RadioWeaves deployment scenarios. This model helps speed up system-level simulators of the RadioWeaves and is made up of two blocks. The first block generates a vector of signalto-interference-plus-noise ratios (SINRs) corresponding to each coherence block, and the second block predicts the packet error rate corresponding to the SINRs generated. The vector of SINRs generated depends on different parameters such as the number of users, user locations, antenna configurations, and precoders. We have also considered different antenna gain patterns, such as omni-directional and directional microstrip patch antennas. Our model exploits the benefits of exponential effective SINR mapping (EESM). We study the robustness and accuracy of the EESM for RadioWeaves.

Place, publisher, year, edition, pages
IEEE , 2022.
Series
IEEE Vehicular Technology Conference VTC, ISSN 1090-3038, E-ISSN 2577-2465
Keywords [en]
RadioWeaves; beyond 5G; cell-free; physical-layer-abstraction; EESM
National Category
Signal Processing
Identifiers
URN: urn:nbn:se:liu:diva-189814DOI: 10.1109/VTC2022-Spring54318.2022.9860923ISI: 000861825802154ISBN: 9781665482431 (electronic)ISBN: 9781665482448 (print)OAI: oai:DiVA.org:liu-189814DiVA, id: diva2:1709422
Conference
IEEE 95th Vehicular Technology Conference: (VTC-Spring), Helsinki, FINLAND, jun 19-22, 2022
Note

Funding Agencies|European Union [101013425]

Available from: 2022-11-08 Created: 2022-11-08 Last updated: 2022-11-08

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Rimalapudi, SarvendranathKunnath Ganesan, UnnikrishnanShaik, Zakir HussainLarsson, Erik G
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CiteExportLink to record
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Citation style
  • apa
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Output format
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