Impulsive noise mitigation for OFDM-based systems using enhanced blanking nonlinearity
Bibliographic record
Abstract
Non-Gaussian impulsive interference mitigation is of great interest in robust and reliable communication over wired and wireless channels. One of the most popular impulsive noise cancellation methods used along with the orthogonal frequency division multiplexing (OFDM) is blanking nonlinearity. Although its simplicity and efficiency, the blanking method suffers from inter-carrier interference (ICI) problem due to its nonlinear characteristic. In this paper, an enhanced version of the blanking technique is proposed to cope with the detrimental effects of nonlinear characteristic of blanking method. To do so, an iterative successive interference cancellation technique is proposed to reconstruct ICI and subtract it from received signal in frequency domain. Simulation results show that the proposed enhancement in blanking method significantly decreases the level of the error floors. For instance, a BER of 1.1 × 10-7is achievable for highly impulsive environments while conventional OFDM systems experience an error floor at 1.8 × 10-2.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| 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 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".