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Cramér-Rao Lower Bounds for Positioning with Large Intelligent Surfaces using Quantized Amplitude and Phase

Alegria, Juan Vidal LU orcid and Rusek, Fredrik LU (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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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
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
2024-06-26 15:17:59
@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}},
}