An OFDMA Baseband Processor Enabling $165\mu \mathrm{W}$ Long-Range IoT Localization

We present an OFDMA baseband localization processor fabricated in 22nm, co-designed with a low-power, crystal-less narrowband RF receiver chip that efficiently estimates the channel frequency response in real-time for localization. The localization processor consumes 1.52/2.04mW for 2/3D localization processing, and duty-cycles the RF receiver achieving 247nW standby power. The combined system achieves a localization distance of 430m, a 4.3× improvement over state-of-the-art, while maintaining comparable accuracy. We deploy this system in a <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$1\times 2.8$</tex> cm IoT localization tag with <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$165\mu \mathrm{W}$</tex> average power at 0.1 Hz location acquisition rate.

Authors

Institutions

Publication Details

Published
2025-06-08
DOI
https://doi.org/10.23919/vlsitechnologyandcir65189.2025.11075164
Citations
1
Primary Topic
Wireless Body Area Networks
Type
article
Field-Weighted Citation Impact
0.67
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

An OFDMA Baseband Processor Enabling $165\mu \mathrm{W}$ Long-Range IoT Localization

Dennis Sylvester, Zichen Fan, Hun-Seok Kim, David Blaauw et al.
1 citations
Wireless Body Area Networks
0.67
article

An OFDMA Baseband Processor Enabling $165\mu \mathrm{W}$ Long-Range IoT Localization

Dennis Sylvester, Zichen Fan, Hun-Seok Kim, David Blaauw, Chien-Wei Tseng, Andrea Bejarano-Carbó, Demba Komma, Pierre Abillama
article en
1 citations

Abstract

We present an OFDMA baseband localization processor fabricated in 22nm, co-designed with a low-power, crystal-less narrowband RF receiver chip that efficiently estimates the channel frequency response in real-time for localization. The localization processor consumes 1.52/2.04mW for 2/3D localization processing, and duty-cycles the RF receiver achieving 247nW standby power. The combined system achieves a localization distance of 430m, a 4.3× improvement over state-of-the-art, while maintaining comparable accuracy. We deploy this system in a <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$1\times 2.8$</tex> cm IoT localization tag with <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$165\mu \mathrm{W}$</tex> average power at 0.1 Hz location acquisition rate.

University of Michigan (US)
Affordable and clean energy
Openalex Percentile: Top 28%
Wireless Body Area Networks
0.67
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.