Cramér-Rao Lower Bounds for Positioning with Large Intelligent Surfaces using Quantized Amplitude and Phase
(2020) 53rd Asilomar Conference on Circuits, Systems and Computers, ACSSC 2019 p.10-14- Abstract
We envision the use of large intelligent surface (LIS) technology, which is a promising concept that goes beyond massive multiple-input multiple-output (MIMO), for positioning applications due to its ability to focus energy in the 3D space. The Cramér-Rao lower bounds (CBLBs) for positioning using a LIS which can resolve amplitude and phase with full resolution have already been determined in the previous literature. However, in real applications, and specially if we consider cheap hardware components to enable the deployment of LIS at reasonable costs, the phase and amplitude have to be quantized before any information can be extracted from them. Furthermore, the phase information is more difficult to resolve due to phase noise,... (More)
We envision the use of large intelligent surface (LIS) technology, which is a promising concept that goes beyond massive multiple-input multiple-output (MIMO), for positioning applications due to its ability to focus energy in the 3D space. The Cramér-Rao lower bounds (CBLBs) for positioning using a LIS which can resolve amplitude and phase with full resolution have already been determined in the previous literature. However, in real applications, and specially if we consider cheap hardware components to enable the deployment of LIS at reasonable costs, the phase and amplitude have to be quantized before any information can be extracted from them. Furthermore, the phase information is more difficult to resolve due to phase noise, non-coherence, etc. In this paper we compute the CRLBs for positioning using LIS with quantized phase and amplitude. We also derive analytical bounds for the CRLB for positioning with LIS when all phase information is disregarded and amplitude is measured with full resolution. We present numerical results in the form of tables including the CRLB loss due to the different quantization resolutions, which can serve as a design guideline for hardware developers.
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- author
- Alegria, Juan Vidal LU and Rusek, Fredrik LU
- organization
- publishing date
- 2020-03-30
- type
- Chapter in Book/Report/Conference proceeding
- publication status
- published
- subject
- keywords
- Cramer-Rao lower bound (CRLB), Large Intelligent Surface (LIS), massive multiple-input multiple-output (MIMO), positioning, quantization
- host publication
- 2019 53rd Asilomar Conference on Signals, Systems, and Computers
- editor
- Matthews, Michael B.
- article number
- 9048973
- pages
- 5 pages
- publisher
- IEEE - Institute of Electrical and Electronics Engineers Inc.
- conference name
- 53rd Asilomar Conference on Circuits, Systems and Computers, ACSSC 2019
- conference location
- Pacific Grove, United States
- conference dates
- 2019-11-03 - 2019-11-06
- external identifiers
-
- scopus:85083296898
- ISBN
- 9781728143002
- 9781728143019
- DOI
- 10.1109/IEEECONF44664.2019.9048973
- language
- English
- LU publication?
- yes
- id
- 6289376d-e39c-4a69-a99c-631efda7d9e0
- date added to LUP
- 2020-05-11 16:59:36
- date last changed
- 2024-09-18 22:54:10
@inproceedings{6289376d-e39c-4a69-a99c-631efda7d9e0, abstract = {{<p>We envision the use of large intelligent surface (LIS) technology, which is a promising concept that goes beyond massive multiple-input multiple-output (MIMO), for positioning applications due to its ability to focus energy in the 3D space. The Cramér-Rao lower bounds (CBLBs) for positioning using a LIS which can resolve amplitude and phase with full resolution have already been determined in the previous literature. However, in real applications, and specially if we consider cheap hardware components to enable the deployment of LIS at reasonable costs, the phase and amplitude have to be quantized before any information can be extracted from them. Furthermore, the phase information is more difficult to resolve due to phase noise, non-coherence, etc. In this paper we compute the CRLBs for positioning using LIS with quantized phase and amplitude. We also derive analytical bounds for the CRLB for positioning with LIS when all phase information is disregarded and amplitude is measured with full resolution. We present numerical results in the form of tables including the CRLB loss due to the different quantization resolutions, which can serve as a design guideline for hardware developers.</p>}}, author = {{Alegria, Juan Vidal and Rusek, Fredrik}}, booktitle = {{2019 53rd Asilomar Conference on Signals, Systems, and Computers}}, editor = {{Matthews, Michael B.}}, isbn = {{9781728143002}}, keywords = {{Cramer-Rao lower bound (CRLB); Large Intelligent Surface (LIS); massive multiple-input multiple-output (MIMO); positioning; quantization}}, language = {{eng}}, month = {{03}}, pages = {{10--14}}, publisher = {{IEEE - Institute of Electrical and Electronics Engineers Inc.}}, title = {{Cramér-Rao Lower Bounds for Positioning with Large Intelligent Surfaces using Quantized Amplitude and Phase}}, url = {{http://dx.doi.org/10.1109/IEEECONF44664.2019.9048973}}, doi = {{10.1109/IEEECONF44664.2019.9048973}}, year = {{2020}}, }