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LoRa-based over-the-air computing for Sat-IoT

Authors :
Universitat Politècnica de Catalunya. Doctorat en Teoria del Senyal i Comunicacions
Universitat Politècnica de Catalunya. Departament de Teoria del Senyal i Comunicacions
Martínez Gost, Marc
Pérez Neira, Ana Isabel
Lagunas Hernandez, Miguel A.
Universitat Politècnica de Catalunya. Doctorat en Teoria del Senyal i Comunicacions
Universitat Politècnica de Catalunya. Departament de Teoria del Senyal i Comunicacions
Martínez Gost, Marc
Pérez Neira, Ana Isabel
Lagunas Hernandez, Miguel A.
Publication Year :
2023

Abstract

Satellite Internet of Things (Sat-IoT) is a novel framework in which satellites integrate sensing, communication and computing capabilities to carry out task-oriented communications. In this paper we propose to use the Long Range (LoRa) modulation for the purpose of estimation in a Sat-IoT scenario. Then we realize that the collisions generated by LoRa can be harnessed in an Over-the-Air Computing (AirComp) framework. Specifically, we propose to use LoRa for Type-based Multiple Access (TBMA), a semantic-aware scheme in which communication resources are assigned to different parameters, not users. Our experimental results show that LoRa-TBMA is suitable as a massive access scheme, provides large gains in terms of mean squared error (MSE) and saves scarce satellite communication resources (i.e., power, latency and bandwidth) with respect to orthogonal multiple access schemes. We also analyze the satellite scenarios that could take advantage of the LoRa-TBMA scheme. In summary, that angular modulations, which are very useful in satellite communications, can also benefit from AirComp.<br />This work is part of the project IRENE (PID2020-115323RB-C31), funded by MCIN/AEI/10.13039/501100011033 and supported by the Catalan government through the project SGR-Cat 2021-01207.<br />Peer Reviewed<br />Postprint (author's final draft)

Details

Database :
OAIster
Notes :
5 p., application/pdf, English
Publication Type :
Electronic Resource
Accession number :
edsoai.on1417305650
Document Type :
Electronic Resource