Mobile Channel Prediction with Application to Transmitter Antenna Selection for Alamouti Systems
Why this work is in the frame
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Bibliographic record
Abstract
Exploiting the full spatial diversity available in mobile wireless channels is most effective when some information about the channel is available at the transmitter. In many practical applications, such information is rapidly outdated and has limited realizable benefits. This note investigates the feasibility of linear fading prediction, applied to noisy channel estimates. The predictions are then used for antenna subset selection for space-time block coding. It is shown, using synthesized and measured channel data, that multidimensional prediction from short channel snapshots is unreliable for dense scattering channels. However, the use of parallel predictors can provide a significant increase in the diversity, and hence performance, achievable with antenna subset selection relative to outdated or no channel information at the transmitter.
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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 | 0.000 | 0.001 |
| 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 it