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Probabilistic Allocation of Payload Code Rate and Header Copies in LR-FHSS Networks

Jamil de Araujo Farhat, Jean Michel de Souza Sant'Ana, João Luiz Rebelatto, Nurul Huda Mahmood, Gianni Pasolini, Richard Demo Souza

TL;DR

Findings reveal that the optimal distribution rarely includes data rate DR9, while data rate DR8 significantly contributes to the goodput and energy efficiency optimizations, while data rate DR8 significantly contributes to the goodput and energy efficiency optimizations.

Abstract

We evaluate the performance of the LoRaWAN Long-Range Frequency Hopping Spread Spectrum (LR-FHSS) technique using a device-level probabilistic strategy for code rate and header replica allocation. Specifically, we investigate the effects of different header replica and code rate allocations at each end-device, guided by a probability distribution provided by the network server. As a benchmark, we compare the proposed strategy with the standardized LR-FHSS data rates DR8 and DR9. Our numerical results demonstrate that the proposed strategy consistently outperforms the DR8 and DR9 standard data rates across all considered scenarios. Notably, our findings reveal that the optimal distribution rarely includes data rate DR9, while data rate DR8 significantly contributes to the goodput and energy efficiency optimizations.

Probabilistic Allocation of Payload Code Rate and Header Copies in LR-FHSS Networks

TL;DR

Findings reveal that the optimal distribution rarely includes data rate DR9, while data rate DR8 significantly contributes to the goodput and energy efficiency optimizations, while data rate DR8 significantly contributes to the goodput and energy efficiency optimizations.

Abstract

We evaluate the performance of the LoRaWAN Long-Range Frequency Hopping Spread Spectrum (LR-FHSS) technique using a device-level probabilistic strategy for code rate and header replica allocation. Specifically, we investigate the effects of different header replica and code rate allocations at each end-device, guided by a probability distribution provided by the network server. As a benchmark, we compare the proposed strategy with the standardized LR-FHSS data rates DR8 and DR9. Our numerical results demonstrate that the proposed strategy consistently outperforms the DR8 and DR9 standard data rates across all considered scenarios. Notably, our findings reveal that the optimal distribution rarely includes data rate DR9, while data rate DR8 significantly contributes to the goodput and energy efficiency optimizations.
Paper Structure (11 sections, 15 equations, 10 figures, 6 tables)

This paper contains 11 sections, 15 equations, 10 figures, 6 tables.

Table of Contents

  1. Introduction
  2. Preliminaries
  3. Proposed Strategy
  4. Network Metrics
  5. Optimization of $\Updelta$
  6. Numerical Results
  7. Goodput Optimization
  8. Energy Efficiency Optimization
  9. Influence of the Payload Length
  10. Quantization of $\Updelta$
  11. Conclusions

Figures (10)

  • Figure 1: End-devices employ varied configurations of header replicas and coding rates to transmit LR-FHSS packets across $c$ physical channels in the frequency domain. The subscript on each header replica ($\mathrm{HD}_m$) and payload fragment ($\mathrm{PL}_m$) indicates its association with the $m$-th user, while the striped boxes illustrate collisions between different devices.
  • Figure 2: Overview of the steps involved in LR-FHSS packet communication process between an end-device and the network server.
  • Figure 3: Network with $M\!=\!10$ end-devices communicating to a gateway, with each end-device using a transmission setup $\mathcal{S}_k$, $k \in \{1,\ldots,6\}$.
  • Figure 4: Success probability versus the number of devices, for DR8, DR9, and the proposed method optimized for the maximum goodput.
  • Figure 5: Goodput versus the number of devices, for DR8, DR9, and the proposed method optimized for either maximum goodput or maximum energy efficiency.
  • ...and 5 more figures