Single and Multiple Continuous-Wave Interference Suppression Using Adaptive IIR Notch Filters Based on Direct-Form Structure in a QPSK Communication System
Bibliographic record
Abstract
The removal filter coefficients in this technique are dependent on the jammer’s power and its Instantaneous Frequency (IF) information, which can both be obtained in the time–frequency domain (adaptive filtering techniques). The dependence of the removing/reducing filter characteristics on the interference power is critical, as it allows an optimal trade-off between removal interference and the amount of self-noise generated by the filter. This trade-off is bounded by the two extreme cases of no notch filter (no self-noise) and full suppression (k1 = 1) for both low- and high-power jammer values. In this paper, a cascade second-order adaptive direct Infinite Impulse Response (IIR) Notch Filter (NF) with a gradient-based algorithm to suppress the Continuous-Wave (CW and MCW) interference is proposed for maximizing the receiver Signal-to-Noise Ratio (SNR) in a Quadrature Phase-Shift Keying (QPSK)-modulated signal. The suppression approach consists of two Adaptive IIR NFs (ANFs) based on a direct-form structure: the Hd1(z) and Hd1(z). The proposal in this work presents a low-complexity Time-Domain (TD) algorithm for controlling the update filter coefficient and notch depth. Simulation results demonstrate that the proposed approach represents an effective method for removing/reducing the impacts of CWI/MCWI, resulting in improved system performance for low- and high-power jammer values when compared with the case of full suppression (k1 = 1); furthermore, it also improves the notch filter’s output SNR for a given Jamming-to-Signal Ratio (JSR) value and Bit Error Ratio (BER) performance. For example, the SNR output of the proposed IIR NF was enhanced by 7 dB versus the case without a filter when Eb/No = 15 dB and JSR = −5 dB. The proposed method can detect and mitigate weak and strong jamming with JSR values ranging from −30 to 40 dB, and can track the hopping frequency interference. Moreover, an improved BER performance is seen as compared to the case without an IIR NF.
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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 | 0.000 |
| 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 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".