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Capture Aware Sequential Waterfilling for LoraWAN Adaptive Data Rate

Giuseppe Bianchi, Francesca Cuomo, Domenico Garlisi, Ilenia Tinnirello

TL;DR

This paper proposes an innovative “sequential waterfilling” strategy for assigning spreading factors to End Devices that yields a significant improvement in the network capacity over the Adaptive Data Rate used by many network operators on the basis of the design suggested by Semtech.

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.

Capture Aware Sequential Waterfilling for LoraWAN Adaptive Data Rate

TL;DR

This paper proposes an innovative “sequential waterfilling” strategy for assigning spreading factors to End Devices that yields a significant improvement in the network capacity over the Adaptive Data Rate used by many network operators on the basis of the design suggested by Semtech.

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.

Paper Structure

This paper contains 16 sections, 14 equations, 9 figures, 2 tables, 1 algorithm.

Table of Contents

  1. Introduction
  2. LoRaWAN: background
  3. Capacity of LoRa Cells
  4. Optimal load allocations across SFs
  5. Effects of inter-SF interference
  6. Capacity Improvements due to channel capture
  7. The EXPLoRa-Capture strategy
  8. EXPLoRa principles: load balancing
  9. EXPLoRa-C details
  10. EXPLoRa-C algorithm
  11. Performance Evaluation
  12. Single cell scenario
  13. Multi-gateway scenario
  14. Application in real scenario
  15. Related Work
  16. ...and 1 more sections

Figures (9)

  • Figure 1: LoRaWAN architecture in case of two gateways with overlapping coverage areas
  • Figure 2: Data Extraction Rate as a function of the number of EDs configured on each SF. When single SF is used in comparison with Aloha formula (a). When only 2 SFs are used, $sf=11$ and $sf=12$ (b) and when 3 SFs are used $sf=10$, $sf=11$ and $sf=12$ (c).
  • Figure 3: Data Extraction Rate as a function of the number of EDs uniformly distributed among all the SFs (a), without channel capture and with channel capture (+ CC). Capture effect for different node distributions (b) and in presence of multiple gateways (c).
  • Figure 4: Nodes position and allocated SF with EXPLoRa-AT (a), EXPLoRa-C single gateway (b) and EXPLoRa-C multi gateway (c).
  • Figure 5: Qualitative representation of the EXPLORA-C mechanism
  • ...and 4 more figures