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Large intelligent surfaces with low-end receivers : from scaling to antenna and panel selection

Sheikhi, Ashkan LU orcid ; Alegría, Juan Vidal LU orcid and Edfors, Ove LU orcid (2025) In IEEE Transactions on Wireless Communications
Abstract

Feasibility of the promising large intelligent surface (LIS) concept, as well as its scalability, relies on the use of low-cost hardware components, raising concerns about the effects of hardware distortion. We analyze LIS systems with receive-chain (RX-chain) hardware distortion, showing how it may limit performance gains when scaling up these systems. In particular, using the memory-less polynomial model, analytical expressions are derived for the signal to noise plus distortion ratio (SNDR) after applying maximum ratio combining (MRC). We also study the effect of back-off and automatic gain control on the RX-chains. The derived expressions enable us to evaluate the scalability of LIS when hardware impairments are present. The cost of... (More)

Feasibility of the promising large intelligent surface (LIS) concept, as well as its scalability, relies on the use of low-cost hardware components, raising concerns about the effects of hardware distortion. We analyze LIS systems with receive-chain (RX-chain) hardware distortion, showing how it may limit performance gains when scaling up these systems. In particular, using the memory-less polynomial model, analytical expressions are derived for the signal to noise plus distortion ratio (SNDR) after applying maximum ratio combining (MRC). We also study the effect of back-off and automatic gain control on the RX-chains. The derived expressions enable us to evaluate the scalability of LIS when hardware impairments are present. The cost of assuming ideal hardware is further analyzed by quantifying the minimum scaling required to achieve the same performance with non-ideal hardware. The analytical expressions derived in this work are also used to propose practical antenna selection schemes for LIS, and we show that such schemes can improve the performance significantly leading to increased energy efficiency. Specifically, by turning off RX-chains with lower contribution to the post-MRC SNDR, we can reduce the energy consumption while maintaining performance. We also consider a more practical scenario where the LIS is deployed as a grid of multi-antenna panels, and we propose panel selection schemes to optimize the complexity-performance trade-offs and improve the system overall efficiency.

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author
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organization
publishing date
type
Contribution to journal
publication status
published
subject
keywords
Hardware distortion, Large intelligent surface, Massive MIMO, Panel selection, Receive antenna selection
in
IEEE Transactions on Wireless Communications
pages
15 pages
publisher
IEEE - Institute of Electrical and Electronics Engineers Inc.
external identifiers
  • scopus:105019955507
ISSN
1536-1276
DOI
10.1109/TWC.2025.3621091
language
English
LU publication?
yes
id
69e972a3-fa48-4c1c-9eb2-a760a02a8bf5
date added to LUP
2025-11-06 12:08:41
date last changed
2025-11-28 12:20:15
@article{69e972a3-fa48-4c1c-9eb2-a760a02a8bf5,
  abstract     = {{<p>Feasibility of the promising large intelligent surface (LIS) concept, as well as its scalability, relies on the use of low-cost hardware components, raising concerns about the effects of hardware distortion. We analyze LIS systems with receive-chain (RX-chain) hardware distortion, showing how it may limit performance gains when scaling up these systems. In particular, using the memory-less polynomial model, analytical expressions are derived for the signal to noise plus distortion ratio (SNDR) after applying maximum ratio combining (MRC). We also study the effect of back-off and automatic gain control on the RX-chains. The derived expressions enable us to evaluate the scalability of LIS when hardware impairments are present. The cost of assuming ideal hardware is further analyzed by quantifying the minimum scaling required to achieve the same performance with non-ideal hardware. The analytical expressions derived in this work are also used to propose practical antenna selection schemes for LIS, and we show that such schemes can improve the performance significantly leading to increased energy efficiency. Specifically, by turning off RX-chains with lower contribution to the post-MRC SNDR, we can reduce the energy consumption while maintaining performance. We also consider a more practical scenario where the LIS is deployed as a grid of multi-antenna panels, and we propose panel selection schemes to optimize the complexity-performance trade-offs and improve the system overall efficiency.</p>}},
  author       = {{Sheikhi, Ashkan and Alegría, Juan Vidal and Edfors, Ove}},
  issn         = {{1536-1276}},
  keywords     = {{Hardware distortion; Large intelligent surface; Massive MIMO; Panel selection; Receive antenna selection}},
  language     = {{eng}},
  month        = {{10}},
  publisher    = {{IEEE - Institute of Electrical and Electronics Engineers Inc.}},
  series       = {{IEEE Transactions on Wireless Communications}},
  title        = {{Large intelligent surfaces with low-end receivers : from scaling to antenna and panel selection}},
  url          = {{http://dx.doi.org/10.1109/TWC.2025.3621091}},
  doi          = {{10.1109/TWC.2025.3621091}},
  year         = {{2025}},
}