A Measurement-Based Spatially Consistent Channel Model for Distributed MIMO in Industrial Environments
arXiv:2412.12646v3 Announce Type: replace Abstract: Future wireless communication systems are envisioned to support ultra-reliable and low-latency communication (URLLC), which will enable new applications such as compute offloading, wireless real-time control, and reliable monitoring. Distributed multiple-input multiple-output (D-MIMO) is one of the most promising technologies for delivering URLLC. This paper classifies obstructions and derives a channel model from a D-MIMO measurement campaign carried out at a carrier frequency of 3.75 GHz with a bandwidth of 35 MHz using twelve fully coherent distributed dipole antennas in an industrial environment. Channel characteristics are investigated, including statistical measures such as small-scale fading, large-scale fading, delay spread, and t
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Abstract:Future wireless communication systems are envisioned to support ultra-reliable and low-latency communication (URLLC), which will enable new applications such as compute offloading, wireless real-time control, and reliable monitoring. Distributed multiple-input multiple-output (D-MIMO) is one of the most promising technologies for delivering URLLC. This paper classifies obstructions and derives a channel model from a D-MIMO measurement campaign carried out at a carrier frequency of 3.75 GHz with a bandwidth of 35 MHz using twelve fully coherent distributed dipole antennas in an industrial environment. Channel characteristics are investigated, including statistical measures such as small-scale fading, large-scale fading, delay spread, and transition rates between line-of-sight and obstructed line-of-sight conditions for the different antenna elements, laying the foundations for an accurate channel model for D-MIMO systems in industrial environments. Furthermore, to ensure spatial consistent simulation results the correlations of large-scale fading between antennas are modeled using Gaussian random fields. Lastly, tail distributions are included to enable proper evaluations of reliability and rare events. Based on the results, a channel model for D-MIMO in industrial environments is presented together with a recipe for its implementation.
Comments: 12 double column pages, 20 figures, Submitted to Transactions on Wireless Communications
Subjects:
Signal Processing (eess.SP)
Cite as: arXiv:2412.12646 [eess.SP]
(or arXiv:2412.12646v3 [eess.SP] for this version)
https://doi.org/10.48550/arXiv.2412.12646
arXiv-issued DOI via DataCite
Submission history
From: Christian Nelson [view email] [v1] Tue, 17 Dec 2024 08:10:32 UTC (6,492 KB) [v2] Tue, 14 Jan 2025 08:31:35 UTC (6,493 KB) [v3] Tue, 31 Mar 2026 14:32:29 UTC (2,260 KB)
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