Performance Analysis of Kalman Filter as an Equalizer in a non-Gaussian environment
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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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".