[Paper Review] Capture Aware Sequential Waterfilling for LoraWAN Adaptive Data Rate

Giuseppe Bianchi, Francesca Cuomo+2 more|arXiv (Cornell University)|Jul 15, 2019

IoT Networks and Protocols21 references4 citations

TL;DR

This paper proposes EXPLoRa-C, a capture-aware sequential waterfilling algorithm for LoRaWAN Adaptive Data Rate that balances spreading factors across multiple gateways while leveraging channel capture to equalize time-on-air per SF group. It achieves up to 38% higher network capacity than legacy ADR, with robust performance across diverse topologies and loads.

ABSTRACT

LoRaWAN (Long Range Wide Area Network) is emerging as an attractive network infrastructure for ultra low power Internet of Things devices. Even if the technology itself is quite mature and specified, the currently deployed wireless resource allocation strategies are still coarse and based on rough heuristics. This paper proposes an innovative "sequential waterfilling" strategy for assigning Spreading Factors (SF) to End-Devices (ED). Our design relies on three complementary approaches: i) equalize the Time-on-Air of the packets transmitted by the system's EDs in each spreading factor's group; ii) balance the spreading factors across multiple access gateways, and iii) keep into account the channel capture, which our experimental results show to be very substantial in LoRa. While retaining an extremely simple and scalable implementation, this strategy yields a significant improvement (up to 38%) in the network capacity over the legacy Adaptive Data Rate (ADR), and appears to be extremely robust to different operating/load conditions and network topology configurations.

Motivation & Objective

• Address the limitations of heuristic-based Adaptive Data Rate (ADR) in LoRaWAN, which fails to exploit channel capture and multi-gateway diversity.
• Improve network capacity by equalizing time-on-air across spreading factor (SF) groups, ensuring balanced resource utilization.
• Leverage the significant channel capture effect in LoRa—where signal strength differences as low as 1 dB enable successful reception—to enhance reliability and throughput.
• Design a scalable, implementation-friendly resource allocation strategy suitable for metropolitan-scale, multi-gateway LoRaWAN deployments.
• Evaluate the proposed scheme under realistic conditions, including real-world deployments of 268 water meters and diverse network topologies.

Proposed method

• Propose a sequential waterfilling algorithm that assigns spreading factors to end-devices (EDs) to equalize time-on-air (ToA) across each SF group, minimizing interference and maximizing spectral efficiency.
• Incorporate channel capture by modeling the probability of successful reception of a stronger signal in the presence of a weaker one, based on experimental measurements showing capture at 1 dB SNR differences.
• Use Received Signal Strength Indicator (RSSI) and network topology data to guide SF assignment, favoring stronger, more reliable links while avoiding collisions.
• Extend the single-gateway EXPLoRa-AT algorithm to a multi-gateway setting by introducing a sequential allocation process that accounts for overlapping coverage and multiple gateway receptions.
• Formulate the resource allocation problem as a constrained optimization task that balances ToA, capture gain, and SF distribution across gateways.
• Validate the algorithm using trace-driven simulations based on real-world LoRaWAN deployments, including data from 268 water meters in Rome, Italy.

Experimental results

Research questions

• RQ1 To what extent can network capacity be improved by equalizing time-on-air across spreading factor groups in LoRaWAN?
• RQ2 How does the channel capture effect in LoRa—particularly its ability to decode a signal with only 1 dB SNR advantage—impact resource allocation efficiency?
• RQ3 Can a sequential waterfilling approach that accounts for multi-gateway reception and capture improve upon legacy ADR in multi-gateway LoRaWAN deployments?
• RQ4 How robust is the proposed scheme across varying network loads, topologies, and real-world deployment conditions?
• RQ5 What is the impact of multiple network operators sharing the same spectrum in a multi-gateway environment on the performance of the proposed allocation strategy?

Key findings

• The proposed EXPLoRa-C algorithm achieves up to 38% higher network capacity compared to legacy Adaptive Data Rate (ADR) under realistic simulation conditions.
• Channel capture in LoRa is highly effective, enabling reliable reception even when the stronger signal is only 1 dB above the weaker one, a key factor exploited by the proposed scheme.
• Equalizing time-on-air across spreading factor groups significantly improves spectral efficiency and reduces interference, especially in high-load scenarios.
• The algorithm demonstrates robust performance across diverse network topologies and load conditions, including multi-gateway and multi-operator deployments.
• The scheme maintains high scalability and simplicity, making it suitable for real-world deployment in metropolitan-scale LoRaWAN networks.
• Simulation results based on real-world data from 268 LoRaWAN water meters confirm the practical viability and performance gains of EXPLoRa-C over existing ADR mechanisms.