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Channel Modeling and Channel Estimation for Holographic Massive MIMO With Planar Arrays
KTH, School of Electrical Engineering and Computer Science (EECS), Computer Science, Communication Systems, CoS.ORCID iD: 0000-0001-9059-2799
KTH, School of Electrical Engineering and Computer Science (EECS), Computer Science, Communication Systems, CoS.ORCID iD: 0000-0002-5954-434x
Univ Pisa, Dipartimento Ingn Informaz, I-56122 Pisa, Italy..
2022 (English)In: IEEE Wireless Communications Letters, ISSN 2162-2337, E-ISSN 2162-2345, Vol. 11, no 5, p. 997-1001Article in journal (Refereed) Published
Abstract [en]

In a realistic wireless environment, the multi-antenna channel usually exhibits spatially correlated fading. This is more emphasized when a large number of antennas is densely deployed, known as holographic massive MIMO (multiple-input multiple-output). In the first part of this letter, we develop a channel model for holographic massive MIMO by considering both non-isotropic scattering and directive antennas. With a large number of antennas, it is difficult to obtain full knowledge of the spatial correlation matrix. In this case, channel estimation is conventionally done using the least-squares (LS) estimator that requires no prior information of the channel statistics or array geometry. In the second part of this letter, we propose a novel channel estimation scheme that exploits the array geometry to identify a subspace of reduced rank that covers the eigenspace of any spatial correlation matrix. The proposed estimator outperforms the LS estimator, without using any user-specific channel statistics.

Place, publisher, year, edition, pages
Institute of Electrical and Electronics Engineers (IEEE) , 2022. Vol. 11, no 5, p. 997-1001
Keywords [en]
Correlation, Antennas, Directive antennas, Scattering, Massive MIMO, Channel estimation, Transmission line matrix methods, Holographic massive MIMO, spatial correlation matrix, planar arrays
National Category
Specific Languages Signal Processing Telecommunications
Identifiers
URN: urn:nbn:se:kth:diva-313026DOI: 10.1109/LWC.2022.3152600ISI: 000793809500028Scopus ID: 2-s2.0-85125338647OAI: oai:DiVA.org:kth-313026DiVA, id: diva2:1662636
Note

QC 20220601

Available from: 2022-06-01 Created: 2022-06-01 Last updated: 2022-06-25Bibliographically approved

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Demir, Ozlem TugfeBjörnson, Emil

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