Fractional Chirp Rate Based CSS Division Multiple Access Over LEO Satellite Internet-of-Things
Bibliographic record
Abstract
Low earth orbit (LEO) satellites are bringing new opportunities for the integration between terrestrial Internet-of-Things (IoT) and satellite IoT. Due to its high robustness against large time delays and Doppler shifts, chirp spread spectrum (CSS) modulation, i.e., the key technology of the Long-Range (LoRa), is expected to empower the satellite link. However, the ALOHA protocol employed by LoRa will inevitably lead to collisions over the satellite channels. In this paper, we focus on the concurrent uplink transmission over the LEO satellite IoT, which is based on CSS. We carefully analyze the relationship between the chirp rate and its spreading factor (SF). Then, we propose the fractional chirp rate based CSS modulation, and support terrestrial users to achieve the non-orthogonal multiple access with the same SF, which ensures that the users possess the same noise immunity. We derive the bit error rate (BER) for both the synchronous and asynchronous scenarios. The performance of our scheme is tested by simulation. Results show that our scheme can achieve the multiple access while maintaining a satisfactory BER performance and is robust over the asynchronous scenario. Furthermore, we build a hardware system using the field-programmable gate array (FPGA) devices to validate the feasibility of this system.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".