A Robust Receiver Based on Chaos Modulation for the Industrial Internet of Things
Bibliographic record
Abstract
Industrial wireless channels feature rich multipath components and strong noise. Massively deployed nodes in an industrial network are often cheap devices. Under such circumstances, the received packets are prone to errors. The conventional method for guaranteeing data quality relies on the MAC layer approach, such as retransmissions. However, this approach creates a data misalignment problem that degrades the performance of multidevice cooperation. Therefore, we propose a robust quadrature ergodic chaotic parameter modulation (QECPM)-based receiver to avoid retransmission. The proposed method does not require timing synchronization. This method eliminates the possibility of cycle slipping, which has a major effect on performance. The bit error rate (BER) performance of the proposed receiver in the Nakagami-m channel is derived and verified by simulation. Using the proposed receiver, the multipath effect can be mitigated using a single scalar. We use software-defined radios (SDRs) to show that the proposed method is robust against timing synchronization errors in practice. Furthermore, we show that as long as there are retransmissions, misaligned packets are to be expected; however, when using the proposed receiver, the error bits are sparse enough to utilize the non-retransmission mode to maintain stable link rates. Our results show that the proposed receiver is robust to multipath, timing synchronization errors and data misalignment.
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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 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".