Reliable Low Power Wide Area Networks-Aided Polar Code
Bibliographic record
Abstract
Low Power Wide Area Network (LPWAN) has emerged recently as a new IoT technology that offers wide coverage connectivity with low power consumption.However, this extraordinarily wide range and low-power performance come at the expense of high latency and low data rate.In 2008, Erdal Arkan proposed a new technique in the forwarding error correction mechanism, called polar codes, which offer a unique advantage of achieving channel capacity with low encoding and decoding complexity, scalability, and a low error floor, making them a compelling choice for error correction compared to other techniques.We used the polar codes mechanism in the LPWAN protocol to enhance error rate and data rate transmission.The performance of our approach demonstrated that polar codes can achieve an improved bit error rate at low data rates compared to the Hamming code technique used in LPWANs.For example, at 125 kHz, the Polar Codes signal-to-noise ratio outperforms the Hamming code used in LPWAN by 18 db.We can observe that at Spreading Factor 8, the Signal to noise ratio showed a better performance for our new code, polar code, with LPWAN for an error rate of 10 -5 .
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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 teacher head, 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".