Message Replication for Improving Reliability of LR-FHSS Direct-to-Satellite IoT

TL;DR

This paper investigates message replication techniques to enhance data transfer reliability in LR-FHSS-based direct-to-satellite IoT networks, avoiding acknowledgment overhead and supporting large uncoordinated device populations.

Contribution

It introduces and compares two message-replication schemes tailored for LR-FHSS D2S-IoT, providing guidelines for optimal method selection based on network traffic.

Findings

Both replication methods improve LR-FHSS reliability.

Conventional replication is better for high traffic scenarios.

Payload-replication suits low traffic, conserving bandwidth.

Abstract

Long-range frequency-hopping spread spectrum (LR-FHSS) promises to enhance network capacity by integrating frequency hopping into existing Long Range Wide Area Networks (LoRaWANs). Due to its simplicity and scalability, LR-FHSS has generated significant interest as a potential candidate for direct-to-satellite IoT (D2S-IoT) applications. This paper explores methods to improve the reliability of data transfer on the uplink (i.e., from terrestrial IoT nodes to satellite) of LR-FHSS D2S-IoT networks. Because D2S-IoT networks are expected to support large numbers of potentially uncoordinated IoT devices per satellite, acknowledgment-cum-retransmission-aided reliability mechanisms are not suitable due to their lack of scalability. We therefore leverage message-replication, wherein every application-layer message is transmitted multiple times to improve the probability of reception without the use of receiver acknowledgments. We propose two message-replication schemes. One scheme is based on conventional replication, where multiple replicas of a message are transmitted, each as a separate link-layer frame. In the other scheme, multiple copies of a message is included in the payload of a single link-layer frame. We show that both techniques improve LR-FHSS reliability. Which method is more suitable depends on the network's traffic characteristics. We provide guidelines to choose the optimal method.