LoRa Resource Allocation Algorithm for Higher Data Rates
Bibliographic record
Abstract
LoRa modulation is a widely used technology known for its long-range transmission capabilities, making it ideal for applications with low data rate requirements, such as IoT-enabled sensor networks. However, its inherent low data rate poses a challenge for applications that require higher throughput, such as video surveillance and disaster monitoring, where large image files must be transmitted over long distances in areas with limited communication infrastructure. In this paper, we introduce the LoRa Resource Allocation (LRA) algorithm, designed to address these limitations by enabling parallel transmissions, thereby reducing the total transmission time (Ttx) and increasing the bit rate (BR). The LRA algorithm leverages the quasi-orthogonality of LoRa’s Spreading Factors (SFs) and employs specially designed end devices equipped with dual LoRa transceivers, each operating on a distinct SF. For experimental analysis we choose an image transmission application and investigate various parameter combinations affecting Ttx to optimize interference, BR, and image quality. Experimental results show that our proposed algorithm reduces Ttx by 42.36% and 19.98% for SF combinations of seven and eight, and eight and nine, respectively. In terms of BR, we observe improvements of 73.5% and 24.97% for these same combinations. Furthermore, BER analysis confirms that the LRA algorithm delivers high-quality images at SNR levels above −5 dB in line-of-sight communication scenarios.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.003 |
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".