SFusion: Energy and Coding Fusion for Ultra-Robust Low-SNR LoRa Networks

TL;DR

SFusion is a novel software framework that significantly enhances LoRa network robustness in low-SNR conditions by combining energy aggregation and coding techniques, outperforming existing solutions.

Contribution

The paper introduces SFusion, a new coding framework that jointly exploits signal-level and coding-level redundancy to improve LoRa performance in extremely weak signal environments.

Findings

Achieves up to 15dB gain over SF12

Provides up to 13dB improvement over state-of-the-art methods

Enhances LoRa robustness in city-scale IoT deployments

Abstract

LoRa has become a cornerstone for city-wide IoT applications due to its long-range, low-power communication. It achieves extended transmission by spreading symbols over multiple samples, with redundancy controlled by the Spreading Factor (SF), and further error resilience provided by Forward Error Correction (FEC). However, practical limits on SF and the separation between signal-level demodulation and coding-level error correction in conventional LoRa PHY leave it vulnerable under extremely weak signals - common in city-scale deployments. To address this, we present SFusion, a software-based coding framework that jointly leverages signal-level aggregation and coding-level redundancy to enhance LoRa's robustness. When signals fall below the decodable threshold, SFusion encodes a quasi-SF(k +m) symbol using 2^m SFk symbols to boost processing gain through energy accumulation. Once partial decoding becomes feasible with energy aggregation, an opportunistic decoding strategy directly combines IQ signals across symbols to recover errors. Extensive evaluations show that SFusion achieves up to 15dB gain over SF12 and up to 13dB improvement over state-of-the-art solutions.