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Modular CSI Quantization for FDD Massive MIMO Communication
Linköping University, Department of Electrical Engineering, Communication Systems. Linköping University, Faculty of Science & Engineering. Aalto Univ, Finland.
Aalto Univ, Finland; Univ Jyvaskyla, Finland.
Aalto Univ, Finland; Univ Nebraska Lincoln, NE 68588 USA.
Huawei Technol Co Ltd, Peoples R China.
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2023 (English)In: IEEE Transactions on Wireless Communications, ISSN 1536-1276, E-ISSN 1558-2248, Vol. 22, no 12, p. 8543-8558Article in journal (Refereed) Published
Abstract [en]

We consider high-dimensional MIMO transmissions in frequency division duplexing (FDD) systems. For precoding, the frequency selective channel has to be measured, quantized and fed back to the base station by the users. When the number of antennas is very high this typically leads to prohibitively high quantization complexity and large feedback. In 5G New Radio (NR), a modular quantization approach has been applied for this, where first a low-dimensional subspace is identified for the whole frequency selective channel, and then subband channels are linearly mapped to this subspace and quantized. We analyze how the components in such a modular scheme contribute to the overall quantization distortion. Based on this analysis we improve the technology components in the modular approach and propose an orthonormalized wideband precoding scheme and a sequential wideband precoding approach which provide considerable gains over the conventional method. We compare the performance of the developed quantization schemes to prior art by simulations in terms of the projection distortion, overall distortion and spectral efficiency, in a scenario with a realistic spatial channel model.

Place, publisher, year, edition, pages
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC , 2023. Vol. 22, no 12, p. 8543-8558
Keywords [en]
Massive MIMO; FDD; CSI quantization
National Category
Telecommunications
Identifiers
URN: urn:nbn:se:liu:diva-201700DOI: 10.1109/TWC.2023.3261754ISI: 001128031700011OAI: oai:DiVA.org:liu-201700DiVA, id: diva2:1845841
Note

Funding Agencies|Huawei Technologies Company Ltd

Available from: 2024-03-20 Created: 2024-03-20 Last updated: 2024-03-20

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CiteExportLink to record
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Citation style
  • apa
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  • de-DE
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