Equalization for WLAN IEEE 802.11b
Bibliographic record
Abstract
In this paper, complementary code keying (CCK) transmission over frequency-selective fading channels is investigated. Since CCK can be interpreted as a block code, an appropriate vector signal model is introduced. Based on pairwise error probabilities, we derive a performance approximation for optimum detection in general frequency-selective Ricean fading channels and compare the theoretical results with simulations. Because optimum maximum-likelihood sequence estimation (MLSE) is not practical for large channel delay spreads, minimum mean-squared error decision-feedback equalization (MMSE-DFE) is employed. Two different DFE approaches are considered here: (a) the conventional scalar DFE and (b) a block-based DFE optimized for the underlying vector signal model. Furthermore, a reduced-state sequence estimation (RSSE) algorithm is designed for CCK. Numerical results are given for fixed channels and common indoor fading channel profiles.
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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".