Adaptive Space-Time Trellis Codes Based on Convolutional Codes
Why this work is in the frame
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Bibliographic record
Abstract
Space-time trellis codes can significantly improve the error performance or the data rate of wireless communications systems with multiple transmit antennas. These codes are usually generated using a generator matrix with 2 columns and modulo 4 operations for QPSK modulation with two transmit antennas. They can also be implemented as Z <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">4</sub> convolutional codes. Another way of representing them is to use a conventional binary convolutional code of rate 2/4. Such a representation is more flexible and allows an easy implementation of space-time encoders and decoders with different modulation schemes and numbers of transmit antennas. Only the rate of the convolutional code needs to be changed using the puncturing technique to accommodate these different schemes. Simulations results have shown that the new codes can achieve an error performance very close to that of the best usual space-time codes.
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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