Adaptive MLSD receiver with identification of flat fading channels
Bibliographic record
Abstract
This paper develops an adaptive maximum likelihood sequence detection (MLSD) algorithm for the Raleigh flat fading environment in association with channel coefficient estimation and channel identification. The design of the MLSD receiver depends on a knowledge of the channel. Along with different channel knowledge assumptions we consider the general case when the channel coefficient is time-variant and the channel statistical characteristics are unknown. The proposed adaptive algorithm has three recursive steps. The channel coefficient is estimated for each path in the trellis diagram by using Kalman filtering; then, based on a dynamic programming algorithm, the transmitted data is detected for each survivor path and, at the final step, the channel is identified by estimating the channel parameters associated with the best previous survivor path. The algorithm is able to track the changes in the channel parameters when the fading rate is changing due to the varying vehicle speed. Performance evaluation and comparisons are considered for different levels of channel knowledge by computer simulation.
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How this classification was reachedexpand
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 | 0.000 | 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 teacher head, 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".