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Multi-user XR offloading via massive MIMO : a system-level analysis using a real-life dataset

Bárány, Love LU ; Yaman, Ilayda LU ; Edfors, Ove LU orcid ; Aminifar, Amir LU orcid and Liu, Liang LU orcid (2026)
Abstract
SLAM is one of the biggest bottlenecks of XR devices, which have strict requirements for latency, power consumption, and user satisfaction. A solution that has been proposed and studied to meet the requirements is to offload SLAM to a remote server, which leverages computational hardware but may suffer due to incurred delays and transmission power. In this work, we propose offloading SLAM using Massive MIMO, which is attractive due to lower latencies, transmission power, and a more reliable link for multiple users. A framework for system-level analysis of latency and localisation error in multi-user offloaded XR with Massive MIMO has been proposed, and a case study with varying system-level parameters has been performed with it. The case... (More)
SLAM is one of the biggest bottlenecks of XR devices, which have strict requirements for latency, power consumption, and user satisfaction. A solution that has been proposed and studied to meet the requirements is to offload SLAM to a remote server, which leverages computational hardware but may suffer due to incurred delays and transmission power. In this work, we propose offloading SLAM using Massive MIMO, which is attractive due to lower latencies, transmission power, and a more reliable link for multiple users. A framework for system-level analysis of latency and localisation error in multi-user offloaded XR with Massive MIMO has been proposed, and a case study with varying system-level parameters has been performed with it. The case study showed that there are important trade-offs between latency, localisation error, and device transmission power. We find that Massive MIMO is a promising technology for XR offloading, but that further evaluations including complete device power consumption are needed to get the full picture. (Less)
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author
; ; ; and
organization
publishing date
type
Working paper/Preprint
publication status
submitted
subject
keywords
Extended reality, Simultaneous localization and Mapping, Massive MIMO, Offloading
pages
5 pages
publisher
arXiv.org
DOI
10.48550/arXiv.2605.02631
project
D-MIMO aided Extended Reality system design
language
English
LU publication?
yes
id
d098335e-ff83-483c-b129-0e9d7068f1dc
date added to LUP
2026-05-07 10:48:01
date last changed
2026-06-04 10:46:15
@misc{d098335e-ff83-483c-b129-0e9d7068f1dc,
  abstract     = {{SLAM is one of the biggest bottlenecks of XR devices, which have strict requirements for latency, power consumption, and user satisfaction. A solution that has been proposed and studied to meet the requirements is to offload SLAM to a remote server, which leverages computational hardware but may suffer due to incurred delays and transmission power. In this work, we propose offloading SLAM using Massive MIMO, which is attractive due to lower latencies, transmission power, and a more reliable link for multiple users. A framework for system-level analysis of latency and localisation error in multi-user offloaded XR with Massive MIMO has been proposed, and a case study with varying system-level parameters has been performed with it. The case study showed that there are important trade-offs between latency, localisation error, and device transmission power. We find that Massive MIMO is a promising technology for XR offloading, but that further evaluations including complete device power consumption are needed to get the full picture.}},
  author       = {{Bárány, Love and Yaman, Ilayda and Edfors, Ove and Aminifar, Amir and Liu, Liang}},
  keywords     = {{Extended reality; Simultaneous localization and Mapping; Massive MIMO; Offloading}},
  language     = {{eng}},
  month        = {{05}},
  note         = {{Preprint}},
  publisher    = {{arXiv.org}},
  title        = {{Multi-user XR offloading via massive MIMO : a system-level analysis using a real-life dataset}},
  url          = {{http://dx.doi.org/10.48550/arXiv.2605.02631}},
  doi          = {{10.48550/arXiv.2605.02631}},
  year         = {{2026}},
}