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Interacting Object-Enabled Clustering and Characterization of Distributed MIMO Channels

Xu, Yingjie LU orcid ; Sandra, Michiel LU ; Cai, Xuesong LU ; Willhammar, Sara LU and Tufvesson, Fredrik LU orcid (2026) In IEEE Transactions on Wireless Communications 25. p.12376-12390
Abstract

Distributed multiple-input multiple-output (MIMO), also known as cell-free massive MIMO, emerges as a promising technology for sixth-generation (6G) systems to support uniform coverage and reliable communication. For the design and optimization of such systems, measurement-based investigations of real-world distributed MIMO channels are essential. In this paper, we present a sub-6 GHz indoor channel measurement campaign, featuring eight distributed antenna arrays with 128 elements in total. Multi-link channels are measured at 50 positions along a 12-meter user route. A clustering algorithm enabled by interacting objects is proposed to identify clusters in the measured channels. The algorithm jointly clusters the multipath components for... (More)

Distributed multiple-input multiple-output (MIMO), also known as cell-free massive MIMO, emerges as a promising technology for sixth-generation (6G) systems to support uniform coverage and reliable communication. For the design and optimization of such systems, measurement-based investigations of real-world distributed MIMO channels are essential. In this paper, we present a sub-6 GHz indoor channel measurement campaign, featuring eight distributed antenna arrays with 128 elements in total. Multi-link channels are measured at 50 positions along a 12-meter user route. A clustering algorithm enabled by interacting objects is proposed to identify clusters in the measured channels. The algorithm jointly clusters the multipath components for all links, effectively capturing the dynamic contributions of common clusters to different links. In addition, a Kalman filter-based tracking framework is introduced for cluster prediction, tracking, and updating along the user movement. Using the clustering and tracking results, cluster-level characterization of the measured channels is performed. First, the number of clusters and their visibility at both link ends are analyzed. Next, a maximum-likelihood estimator is utilized to determine the entire cluster visibility region length. Finally, key cluster-level properties, including the common cluster ratio, cluster power, shadowing, spread, among others, are statistically investigated. The results provide valuable insights into cluster behavior in typical multi-link channels, necessary for accurate modeling of sub-6 GHz distributed MIMO channels.

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author
; ; ; and
organization
publishing date
type
Contribution to journal
publication status
published
subject
keywords
Cell-free massive MIMO, cluster-level channel characterization, clustering algorithm, distributed MIMO, multi-link channel measurements
in
IEEE Transactions on Wireless Communications
volume
25
pages
15 pages
publisher
IEEE - Institute of Electrical and Electronics Engineers Inc.
external identifiers
  • scopus:105031504391
ISSN
1536-1276
DOI
10.1109/TWC.2026.3665431
language
English
LU publication?
yes
additional info
Publisher Copyright: © 2002-2012 IEEE.
id
75fcedc7-00a8-4050-9d74-00e755f4e719
date added to LUP
2026-04-21 15:58:41
date last changed
2026-04-21 15:59:27
@article{75fcedc7-00a8-4050-9d74-00e755f4e719,
  abstract     = {{<p>Distributed multiple-input multiple-output (MIMO), also known as cell-free massive MIMO, emerges as a promising technology for sixth-generation (6G) systems to support uniform coverage and reliable communication. For the design and optimization of such systems, measurement-based investigations of real-world distributed MIMO channels are essential. In this paper, we present a sub-6 GHz indoor channel measurement campaign, featuring eight distributed antenna arrays with 128 elements in total. Multi-link channels are measured at 50 positions along a 12-meter user route. A clustering algorithm enabled by interacting objects is proposed to identify clusters in the measured channels. The algorithm jointly clusters the multipath components for all links, effectively capturing the dynamic contributions of common clusters to different links. In addition, a Kalman filter-based tracking framework is introduced for cluster prediction, tracking, and updating along the user movement. Using the clustering and tracking results, cluster-level characterization of the measured channels is performed. First, the number of clusters and their visibility at both link ends are analyzed. Next, a maximum-likelihood estimator is utilized to determine the entire cluster visibility region length. Finally, key cluster-level properties, including the common cluster ratio, cluster power, shadowing, spread, among others, are statistically investigated. The results provide valuable insights into cluster behavior in typical multi-link channels, necessary for accurate modeling of sub-6 GHz distributed MIMO channels.</p>}},
  author       = {{Xu, Yingjie and Sandra, Michiel and Cai, Xuesong and Willhammar, Sara and Tufvesson, Fredrik}},
  issn         = {{1536-1276}},
  keywords     = {{Cell-free massive MIMO; cluster-level channel characterization; clustering algorithm; distributed MIMO; multi-link channel measurements}},
  language     = {{eng}},
  pages        = {{12376--12390}},
  publisher    = {{IEEE - Institute of Electrical and Electronics Engineers Inc.}},
  series       = {{IEEE Transactions on Wireless Communications}},
  title        = {{Interacting Object-Enabled Clustering and Characterization of Distributed MIMO Channels}},
  url          = {{http://dx.doi.org/10.1109/TWC.2026.3665431}},
  doi          = {{10.1109/TWC.2026.3665431}},
  volume       = {{25}},
  year         = {{2026}},
}