Hardware distortion modeling for panel selection in large intelligent surfaces
(2024) 58th Asilomar Conference on Signals, Systems and Computers, ACSSC 2024 In Conference Record p.822-826- Abstract
Hardware distortion in large intelligent surfaces (LISs) may limit their performance when scaling up such systems. It is of great importance to model the non-ideal effects in their transceivers to study the hardware distortions that can affect their performance. Therefore, we have focused on modeling and studying the effects of nonlinear RX-chains in LISs. We first derive expressions for SNDR of a LIS with a memory-less polynomial-based model at its RX-chains. Then we propose a simplified double-parameter exponential model for the distortion power and show that compared to the polynomial based model, the exponential model can improve the analytical tractability for SNDR optimization problems. In particular, we consider a panel selection... (More)
Hardware distortion in large intelligent surfaces (LISs) may limit their performance when scaling up such systems. It is of great importance to model the non-ideal effects in their transceivers to study the hardware distortions that can affect their performance. Therefore, we have focused on modeling and studying the effects of nonlinear RX-chains in LISs. We first derive expressions for SNDR of a LIS with a memory-less polynomial-based model at its RX-chains. Then we propose a simplified double-parameter exponential model for the distortion power and show that compared to the polynomial based model, the exponential model can improve the analytical tractability for SNDR optimization problems. In particular, we consider a panel selection optimization problems in a panel-based LIS scenario and show that the proposed model enables us to derive two closed-form sub-optimal solutions for panel selection, and can be a favorable alternative to high-order polynomial models in terms of computation complexity, especially for theoretical works on hardware distortion in multiple-input multiple-output (MIMO) and LIS systems. Numerical results show that the sub-optimal closed-form solutions have a near-optimal performance in terms of SNDR compared to the global optimum found by high-complexity heuristic search methods.
(Less)
- author
- Sheikhi, Ashkan
LU
; Alegría, Juan Vidal LU
and Edfors, Ove LU
- organization
- publishing date
- 2024
- type
- Chapter in Book/Report/Conference proceeding
- publication status
- published
- subject
- keywords
- Hardware distortion, Large intelligent surface, MIMO, Panel selection
- host publication
- 2024 58th Asilomar Conference on Signals, Systems, and Computers
- series title
- Conference Record
- pages
- 5 pages
- publisher
- IEEE - Institute of Electrical and Electronics Engineers Inc.
- conference name
- 58th Asilomar Conference on Signals, Systems and Computers, ACSSC 2024
- conference location
- Hybrid, Pacific Grove, United States
- conference dates
- 2024-10-27 - 2024-10-30
- external identifiers
-
- scopus:105002694613
- ISSN
- 1058-6393
- ISBN
- 979-8-3503-5405-8
- 979-8-3503-5406-5 (print-on-demand)
- DOI
- 10.1109/IEEECONF60004.2024.10942661
- language
- English
- LU publication?
- yes
- id
- 7b187ffc-74d9-4107-bbd5-f399d6c3a83c
- date added to LUP
- 2025-08-17 19:13:29
- date last changed
- 2025-09-28 23:46:46
@inproceedings{7b187ffc-74d9-4107-bbd5-f399d6c3a83c, abstract = {{<p>Hardware distortion in large intelligent surfaces (LISs) may limit their performance when scaling up such systems. It is of great importance to model the non-ideal effects in their transceivers to study the hardware distortions that can affect their performance. Therefore, we have focused on modeling and studying the effects of nonlinear RX-chains in LISs. We first derive expressions for SNDR of a LIS with a memory-less polynomial-based model at its RX-chains. Then we propose a simplified double-parameter exponential model for the distortion power and show that compared to the polynomial based model, the exponential model can improve the analytical tractability for SNDR optimization problems. In particular, we consider a panel selection optimization problems in a panel-based LIS scenario and show that the proposed model enables us to derive two closed-form sub-optimal solutions for panel selection, and can be a favorable alternative to high-order polynomial models in terms of computation complexity, especially for theoretical works on hardware distortion in multiple-input multiple-output (MIMO) and LIS systems. Numerical results show that the sub-optimal closed-form solutions have a near-optimal performance in terms of SNDR compared to the global optimum found by high-complexity heuristic search methods.</p>}}, author = {{Sheikhi, Ashkan and Alegría, Juan Vidal and Edfors, Ove}}, booktitle = {{2024 58th Asilomar Conference on Signals, Systems, and Computers}}, isbn = {{979-8-3503-5405-8}}, issn = {{1058-6393}}, keywords = {{Hardware distortion; Large intelligent surface; MIMO; Panel selection}}, language = {{eng}}, pages = {{822--826}}, publisher = {{IEEE - Institute of Electrical and Electronics Engineers Inc.}}, series = {{Conference Record}}, title = {{Hardware distortion modeling for panel selection in large intelligent surfaces}}, url = {{http://dx.doi.org/10.1109/IEEECONF60004.2024.10942661}}, doi = {{10.1109/IEEECONF60004.2024.10942661}}, year = {{2024}}, }