A Markov-Middleton Model for Bursty Impulsive Noise: Modeling and Receiver Design
Bibliographic record
Abstract
Transmission over channels impaired by impulsive noise, such as in power substations, calls for peculiar mitigation techniques at the receiver side in order to cope with signal deterioration. For these techniques to be effective, a reliable noise model is usually required. One of the widely accepted models is the Middleton Class A, which presents the twofold advantage to be canonical (i.e., invariant of the particular physical source mechanisms) and to exhibit a simple probability density function (PDF) that only depends on three physical parameters, making this model very attractive. However, such a model fails in replicating bursty impulsive noise, where each impulse spans over several consecutive noise samples, as usually observed (e.g., in power substations). Indeed, the Middleton Class A model only deals with amplitude or envelope statistics. On the other hand, for models based on Markov chains, although they reproduce the bursty nature of impulses, the determination of the suitable number of states and the noise distribution associated with each state can be challenging. In this paper, 1) we introduce a new impulsive noise model which is, in fact, a Hidden Markov Model, whose realizations exactly follow a Middleton Class A distribution and 2) we evaluate optimum and suboptimum detections for a coded transmission impaired by the proposed noise model.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 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".