Evaluating Energy Efficiency in LR-FHSS Networks under Successive Interference Cancellation
Bibliographic record
Abstract
Low Rate Frequency Hopping Spread Spectrum (LR-FHSS) has emerged as a promising physical layer modulation for enhancing the scalability and robustness of LoRaWAN-based IoT networks. While its benefits in terms of interference mitigation and network capacity have been well documented, its impact on energy efficiency remains underexplored. This paper presents an analytical and simulation-based investigation of energy efficiency in LR-FHSS networks, taking into account key transmission parameters such as header repetitions and the application of Successive Interference Cancellation (SIC). We first develop an analytical model that quantifies energy efficiency as the ratio of goodput to total power consumption, incorporating the stochastic nature of traffic, fragmentation structure, and collision probability. The model is then validated and extended through simulations Our results highlight the trade-offs between reliability and energy consumption under varying network loads and header repetition strategies. Notably, we demonstrate that integrating SIC into LR-FHSS significantly enhances energy efficiency without modifying end-device configurations. Finally, we identify the optimal configuration of header repetitions that balances energy usage and network reliability, thereby guiding future deployments of large-scale IoT systems leveraging LR-FHSS.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".