Analysis and Comparison of Several Mitigation Techniques for Middleton Class-A Noise
Bibliographic record
Abstract
Impulsive noise is a common impediment in many wireless, power line communication (PLC), and smart grid communication systems that prevents the system from achieving error-free transmission. In this paper, we compare and analyze several impulsive noise mitigation techniques for Middleton class-A noise considering single carrier modulation with low-density parity-check (LDPC) coded transmission. For this, we investigate the widely used non-linear methods such as clipping, blanking, and combined clipping/blanking to mitigate the noxious effects of impulsive noise. Although, the performance of these techniques are widely acknowledged for simple Bernoulli-Gaussian impulsive noise mitigation in case of orthogonal frequency division multiplexing (OFDM)-based multi-carrier communication systems, their mitigation capabilities in regards to Middleton class-A noise remains unknown. We further investigate the log-likelihood ratio (LLR)-based impulsive noise mitigation. Simulation results are provided to highlight the robustness of the LLR-based mitigation scheme over simple clipping/blanking schemes for the considered scenario. Moreover, our results show that while clipping performs better than blanking for Bernoulli-Gaussian noise, the later shows better performance in case of Middleton class-A noise.
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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) | 0.000 | 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".