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Spectrum Efficiency and Processing Latency Trade-offs in Panel-Based LIS

Tinnerberg, Lina LU ; Iancu, Dumitra LU ; Edfors, Ove LU orcid ; Liu, Liang LU orcid and Alegría, Juan Vidal LU orcid (2025) 59th Asilomar Conference on Signals, Systems and Computers, ACSSC 2025 p.519-524
Abstract

The next generation wireless systems will face stringent new requirements, including ultra-low latency, high data rates and enhanced reliability. Large Intelligent Surfaces, is one proposed solution that has the potential to solve these high demands. The real-life deployment of such systems involves different design considerations with non-trivial trade-offs. This paper investigates the trade-off between spectral efficiency and processing latency, considering different antenna distribution schemes and detection algorithms. A latency model for the physical layer processing has been developed, using real FPGA and application-specific instruction processor (ASIP) hardware implementation results. Simulation results using an indoor... (More)

The next generation wireless systems will face stringent new requirements, including ultra-low latency, high data rates and enhanced reliability. Large Intelligent Surfaces, is one proposed solution that has the potential to solve these high demands. The real-life deployment of such systems involves different design considerations with non-trivial trade-offs. This paper investigates the trade-off between spectral efficiency and processing latency, considering different antenna distribution schemes and detection algorithms. A latency model for the physical layer processing has been developed, using real FPGA and application-specific instruction processor (ASIP) hardware implementation results. Simulation results using an indoor environment show that distributing antennas throughout the scenario improves overall reliability, while the impact from this on latency is limited both when using zero-forcing (ZF) and Minimum Mean Square Error (MMSE) detection. Changing the detection algorithm to maximum-ratio combining (MRC) from ZF or MMSE, however, reduces the latency significantly, even if a larger number of antennas are needed to achieve a similar spectrum efficiency.

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Please use this url to cite or link to this publication:
author
; ; ; and
organization
publishing date
type
Chapter in Book/Report/Conference proceeding
publication status
published
subject
keywords
6G, Decentralized Processing, Distributed Massive MIMO, Latency, Panel-Based Large Intelligent Surface (LIS)
host publication
Conference Record of the 59th Asilomar Conference on Signals, Systems and Computers, ACSSC 2025
editor
Matthews, Michael B.
pages
6 pages
publisher
IEEE Computer Society
conference name
59th Asilomar Conference on Signals, Systems and Computers, ACSSC 2025
conference location
Pacific Grove, United States
conference dates
2025-10-26 - 2025-10-29
external identifiers
  • scopus:105035826699
ISBN
9798331587451
DOI
10.1109/IEEECONF67917.2025.11443936
language
English
LU publication?
yes
id
88094d30-eade-495a-bd70-38db7e93669c
date added to LUP
2026-06-23 14:36:57
date last changed
2026-06-23 14:37:30
@inproceedings{88094d30-eade-495a-bd70-38db7e93669c,
  abstract     = {{<p>The next generation wireless systems will face stringent new requirements, including ultra-low latency, high data rates and enhanced reliability. Large Intelligent Surfaces, is one proposed solution that has the potential to solve these high demands. The real-life deployment of such systems involves different design considerations with non-trivial trade-offs. This paper investigates the trade-off between spectral efficiency and processing latency, considering different antenna distribution schemes and detection algorithms. A latency model for the physical layer processing has been developed, using real FPGA and application-specific instruction processor (ASIP) hardware implementation results. Simulation results using an indoor environment show that distributing antennas throughout the scenario improves overall reliability, while the impact from this on latency is limited both when using zero-forcing (ZF) and Minimum Mean Square Error (MMSE) detection. Changing the detection algorithm to maximum-ratio combining (MRC) from ZF or MMSE, however, reduces the latency significantly, even if a larger number of antennas are needed to achieve a similar spectrum efficiency.</p>}},
  author       = {{Tinnerberg, Lina and Iancu, Dumitra and Edfors, Ove and Liu, Liang and Alegría, Juan Vidal}},
  booktitle    = {{Conference Record of the 59th Asilomar Conference on Signals, Systems and Computers, ACSSC 2025}},
  editor       = {{Matthews, Michael B.}},
  isbn         = {{9798331587451}},
  keywords     = {{6G; Decentralized Processing; Distributed Massive MIMO; Latency; Panel-Based Large Intelligent Surface (LIS)}},
  language     = {{eng}},
  pages        = {{519--524}},
  publisher    = {{IEEE Computer Society}},
  title        = {{Spectrum Efficiency and Processing Latency Trade-offs in Panel-Based LIS}},
  url          = {{http://dx.doi.org/10.1109/IEEECONF67917.2025.11443936}},
  doi          = {{10.1109/IEEECONF67917.2025.11443936}},
  year         = {{2025}},
}