Bibliographic record
Abstract
High rate linear dispersion codes (LDC) for space time channels can support arbitrary numbers of transmit and receive antennas. In contrast to the one-to-one transformations used in interleaving, these codes disperse data in linear combinations over space and time. To improve performance of orthogonal frequency division multiplexing (OFDM) for wireless fading channels, this paper investigates increasing frequency and time diversity using LDC. To overcome the requirement of constant channel gains over an entire LDC time interval, a new decoding algorithm for a special subclass of LDC is proposed. The newly proposed LDC-OFDM linearly disperses data over both time and frequency, i.e., over multiple subcarriers and OFDM blocks. Simulations show the bit error rate (BER) performance of rate-one LDC-OFDM with zero padding is superior to that of uncoded OFDM with zero padding. Further, compared to uncoded OFDM, LDC-OFDM may have improved performance without increasing the peak-to-average power ratio (PAPR).
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.002 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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 source (direct Gemma or distilled Codex), 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".