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Fragment-Level Macro-Diversity Reception in LoRaWAN Networks with LR-FHSS

Samer Lahoud, Kinda Khawam

TL;DR

LR-FHSS expands LoRaWAN capacity through header repetition and payload fragmentation with erasure coding, but single-gateway decoding limits scalability in dense IoT networks. The authors develop a stochastic-geometry framework to model fragment-level macro-diversity, deriving closed-form packet-success probabilities for headers and payloads under LR-FHSS parameters. Results show substantial capacity and goodput gains over nearest-gateway reception, with DR5 offering stronger robustness at high load and DR6 delivering higher peak throughput before saturation. This work provides a theoretical foundation for scalable LR-FHSS deployments and guides design choices for redundancy, data rate, and multi-gateway coordination.

Abstract

The rapid expansion of Internet of Things (IoT) deployments demands wireless protocols that combine high scalability with robust performance. Long Range-Frequency Hopping Spread Spectrum (LR-FHSS) extends LoRaWAN by increasing capacity and resilience through frequency hopping and redundancy. However, current deployments require packet reconstruction at a single gateway, limiting the benefits of LR-FHSS. This paper proposes a macro-diversity reception strategy where multiple gateways collectively receive and combine payload fragments. We develop a stochastic geometry-based analytical model that captures the impact of header repetition, payload fragmentation, and coding redundancy. Closed-form expressions quantify success probabilities under interference, and numerical evaluations demonstrate significant capacity gains over nearest-gateway reception. These results highlight the potential of fragment-level macro-diversity to improve scalability and reliability in future LPWAN deployments.

Fragment-Level Macro-Diversity Reception in LoRaWAN Networks with LR-FHSS

TL;DR

LR-FHSS expands LoRaWAN capacity through header repetition and payload fragmentation with erasure coding, but single-gateway decoding limits scalability in dense IoT networks. The authors develop a stochastic-geometry framework to model fragment-level macro-diversity, deriving closed-form packet-success probabilities for headers and payloads under LR-FHSS parameters. Results show substantial capacity and goodput gains over nearest-gateway reception, with DR5 offering stronger robustness at high load and DR6 delivering higher peak throughput before saturation. This work provides a theoretical foundation for scalable LR-FHSS deployments and guides design choices for redundancy, data rate, and multi-gateway coordination.

Abstract

The rapid expansion of Internet of Things (IoT) deployments demands wireless protocols that combine high scalability with robust performance. Long Range-Frequency Hopping Spread Spectrum (LR-FHSS) extends LoRaWAN by increasing capacity and resilience through frequency hopping and redundancy. However, current deployments require packet reconstruction at a single gateway, limiting the benefits of LR-FHSS. This paper proposes a macro-diversity reception strategy where multiple gateways collectively receive and combine payload fragments. We develop a stochastic geometry-based analytical model that captures the impact of header repetition, payload fragmentation, and coding redundancy. Closed-form expressions quantify success probabilities under interference, and numerical evaluations demonstrate significant capacity gains over nearest-gateway reception. These results highlight the potential of fragment-level macro-diversity to improve scalability and reliability in future LPWAN deployments.
Paper Structure (15 sections, 19 equations, 5 figures, 1 table)

This paper contains 15 sections, 19 equations, 5 figures, 1 table.

Table of Contents

  1. Introduction
  2. Related Works
  3. LR-FHSS Modulation
  4. Data Rates and Coding
  5. Packet Structure
  6. Channelization and Subcarrier Grid
  7. Frequency Hopping Strategy
  8. System Model
  9. SINR Model
  10. Proposed Macro-Diversity Reception Strategy
  11. Analytical Model of Macro-Diversity Reception
  12. Header Reception
  13. Payload Reception
  14. Numerical Evaluation
  15. Conclusion and Outlook on Macro-Diversity

Figures (5)

  • Figure 1: Spectrogram of an LR-FHSS packet showing sequential header replicas followed by pseudo-randomly hopped payload fragments.
  • Figure 2: Proposed macro-diversity reception with controller-assisted fragment collection. Gateways that decode a header forward it to the controller, which retrieves the hopping sequence and instructs all gateways to track the corresponding fragments. All received fragments are forwarded to the controller, which performs de-duplication and centralized packet reconstruction.
  • Figure 3: Total packet success probability as a function of load per gateway. Macro-diversity significantly extends the reliable operating region for both DR5 and DR6.
  • Figure 4: Header and payload success probabilities under macro-diversity. At high success probabilities, header decoding is the limiting factor in DR5, while payload decoding limits performance in DR6.
  • Figure 5: Goodput per gateway for macro-diversity and nearest-gateway reception. DR6 peaks higher but saturates earlier; DR5 sustains performance at heavier loads. Macro-diversity improves peak goodput by up to 5 times and extends the sustainable load range.