SFIxM: Flexible LoRa Modulations for Elastic Resource Allocation

This paper presents SFIxM, a flexible evolution of the LoRa modulation scheme designed to facilitate the allocation of transmission parameters across nodes within a gateway’s coverage area. SFIxM increases the number of available quasi-orthogonal channels and, unlike standard LoRa, offers tunable robustness to noise and interference. Because SFIxM introduces a new requirement for time-axis alignment between transmitter and receiver, we present a practical procedure to achieve this synchronisation, along with an analytical estimate of its failure probability, which we find negligible. Based on this foundation, we show that SFIxM provides traffic capacity gains of 40 to 55%, comparable to the gains obtained with receiver diversity, a well-known approach to improving coverage and range, which also turns out to combine well with SFIxM .

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Publication Details

Published
2025-10-27
DOI
https://doi.org/10.1109/mswim67937.2025.11308894
Primary Topic
IoT Networks and Protocols
Type
article
Field-Weighted Citation Impact
0.00

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article

SFIxM: Flexible LoRa Modulations for Elastic Resource Allocation

Christelle Caillouet, Martin Heusse, G. Maury
IoT Networks and Protocols
article

SFIxM: Flexible LoRa Modulations for Elastic Resource Allocation

Christelle Caillouet, Martin Heusse, G. Maury
article en

Abstract

This paper presents SFIxM, a flexible evolution of the LoRa modulation scheme designed to facilitate the allocation of transmission parameters across nodes within a gateway’s coverage area. SFIxM increases the number of available quasi-orthogonal channels and, unlike standard LoRa, offers tunable robustness to noise and interference. Because SFIxM introduces a new requirement for time-axis alignment between transmitter and receiver, we present a practical procedure to achieve this synchronisation, along with an analytical estimate of its failure probability, which we find negligible. Based on this foundation, we show that SFIxM provides traffic capacity gains of 40 to 55%, comparable to the gains obtained with receiver diversity, a well-known approach to improving coverage and range, which also turns out to combine well with SFIxM .

Institut polytechnique de Grenoble (FR), Centre National de la Recherche Scientifique (FR), Institut national de recherche en sciences et technologies du numérique (FR), Laboratoire d'Informatique de Grenoble (FR), Université Savoie Mont Blanc (FR), Université Grenoble Alpes (FR)
Agence Nationale de la Recherche
Openalex Percentile: Top 72%
IoT Networks and Protocols
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