An LLR-Based Receiver for Mitigating Bursty Impulsive Noise With Unknown Distributions
Bibliographic record
Abstract
The rapid expansion of Internet of Things (IoT) networks has paved the way for their integration into mission-critical applications requiring secure and reliable monitoring, such as smart grid utilities. However, these advanced power grids face significant challenges in maintaining reliable wireless communication, particularly in hostile environments like high-voltage substations and power plants. These environments are characterized by intense bursts of interference, known as impulsive noise with memory. To address this problem, in this study, we introduce a two-process receiver design. The first process is a multi-step receiver parameter estimation process. The second process is a novel memory-aware log-likelihood ratio (LLR) calculation method designed to mitigate the effects of impulsive noise with memory using the parameters estimated from the first process. This method is computationally efficient, which makes it suitable for IoT devices with limited computational capabilities. Simulation results obtained show that the proposed method achieves a bit error rate (BER) similar to the corresponding BERs of the best-performing algorithms with perfect noise parameters. Furthermore, it outperforms the Viterbi algorithm amid imperfect noise parameters. Notably, it method achieves these benchmarks while substantially improving execution time.
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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 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 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) | 0.001 | 0.001 |
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".