Super-LoRa: Enhancing LoRa Throughput via Payload Superposition

Fuente: arXiv
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Main Authors: Abdeljabar, Salah, Alouini, Mohamed-Slim
Format: Preprint
Published: 2025
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author Abdeljabar, Salah
Alouini, Mohamed-Slim
author_facet Abdeljabar, Salah
Alouini, Mohamed-Slim
contents This paper presents Super-LoRa, a novel approach to enhancing the throughput of LoRa networks by leveraging the inherent robustness of LoRa modulation against interference. By superimposing multiple payload symbols, Super-LoRa significantly increases the data rate while maintaining lower transmitter and receiver complexity. Our solution is evaluated through both simulations and real-world experiments, showing a potential throughput improvement of up to 5x compared to standard LoRa. This advancement positions Super-LoRa as a viable solution for data-intensive IoT applications such as smart cities and precision agriculture, which demand higher data transmission rates.
format Preprint
id arxiv_https___arxiv_org_abs_2504_11927
institution arXiv
publishDate 2025
record_format arxiv
spellingShingle Super-LoRa: Enhancing LoRa Throughput via Payload Superposition
Abdeljabar, Salah
Alouini, Mohamed-Slim
Signal Processing
This paper presents Super-LoRa, a novel approach to enhancing the throughput of LoRa networks by leveraging the inherent robustness of LoRa modulation against interference. By superimposing multiple payload symbols, Super-LoRa significantly increases the data rate while maintaining lower transmitter and receiver complexity. Our solution is evaluated through both simulations and real-world experiments, showing a potential throughput improvement of up to 5x compared to standard LoRa. This advancement positions Super-LoRa as a viable solution for data-intensive IoT applications such as smart cities and precision agriculture, which demand higher data transmission rates.
title Super-LoRa: Enhancing LoRa Throughput via Payload Superposition
topic Signal Processing
url https://arxiv.org/abs/2504.11927