Optimized Delay Diversity for Suboptimum Equalization
Bibliographic record
Abstract
The optimization of delay diversity (DD) for linear equalization (LE) and decision-feedback equalization (DFE) is presented. The general case of transmission over a correlated multiple-input-multiple-output frequency-selective fading channel is considered. The proposed optimization requires the knowledge of the statistical properties of the wireless channel at the transmitter, but channel state information is only required at the receiver side. Based on an approximation of the bit error rate for LE and DFE, a stochastic gradient algorithm for optimization of the DD transmit filters is derived. Simulation results for the Global System for Mobile Communications (GSM)/Enhanced Data Rates for GSM Evolution system show significant performance gains of the proposed optimized DD scheme over the DD schemes reported by Gore et al. (Proc. IEEE Inter. Conf. Commun., 2002) and Hehn et al. (IEEE Trans. Wireless Commun., vol. 4, p. 2289, 2005) if LE and DFE are used at the receiver
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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".