Performance Analysis of Kalman Filter as an Equalizer in a non-Gaussian environment
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Bibliographic record
Abstract
This paper analyzed the MSE and BER performances of communication systems which used Kalman Filtering as a channel equalizer in non-Gaussian noise environment. In telecommunication systems, fading and additive noise are two critical factors that significantly impacts on the system performance. Most of existing receiver have been designed to well-handle the AWGN noise, thus, such systems may suffer several performance losses when other noise types as impulsive noises present. The proposed algorithm applies the Kalman filter-based equalizer to overcome the impact of non-Gaussian noise. Multiple non-Gaussian noise models have been developed, among them, Middleton’s Class A noise is chosen in the scope of this paper. A Rayleigh flat-fading channel is simulated using autoregressive model approach which makes Kalman filtering being usable. The BER and MSE performances of Kalman equalizer under subjected non-Gaussian noise is analyzed for various SNR and parameters scenarios. Simulation results show that the performance of Kalman equalizer is impacted by the overlapped index and the ratio of Gaussian noise power over Impulsive noise power under class A noise. In the high SNR region, BER performance is significantly impacted by impulsive component and in the low SNR region, the performance is mainly impacted by Gaussian component.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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 it