Comparing the Performances between Adaptive Notch Filter Direct and Lattice Forms Structures for Mitigation Jamming Signals
Bibliographic record
Abstract
A jamming signal such as single and multiple Continuous-Wave (CW and MCW) interferences have been shown to have severe effects on the quality of the received signal in wireless communication. This paper presents an approach of a low-complexity algorithm that compares the performances of using Adaptive Notch Filter (ANF) direct and lattice forms structures based on second-order Infinite Impulse Response (IIR) Notch Filter (NF) for the detection and mitigation of CW and MCW interferences in QPSK communication systems. The approach method consists of two ANFs, adaptive IIR NF and adaptive IIR NF . The present algorithm can estimate and mitigate each CWI and computer their power in Time-Domain (TD). In results for performance comparison, the lattice IIR NF structure outperforms the direct IIR NF structure for detection and removal jamming and has a better Bit Error Ratio (BER). Furthermore, compared with the case of full suppression (), both cases (direct and lattice form) work better for low and high-power jammers. Also, compared to the case without an IIR NF, the presented algorithm can detect and mitigate, track hopping frequency interference, and improve BER performance.
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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 | 0.000 |
| 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".