Bibliographic record
Abstract
This paper investigates increasing space, time, and frequency diversity through linear dispersion codes (LDC) in MIMO-OFDM wireless fading channels. Two new types of block-based high-rate space-time-frequency (STF) codes: (1) double linear dispersion space-time-frequency-coding (DLD-STFC), and (2) linear dispersion space-time-frequency-coding (LD-STFC) are proposed. In addition to double LDC encoding, DLD-STFC uses three-stage LDC decoding. The LD-STFC, on the other hand, requires only one LDC procedure across multiple OFDM subcarriers, OFDM blocks and multiple antennas. Both DLD-STFC and LD-STFC are backwards compatible to uncoded MIMO-OFDM systems. Comparison to an extension of a recently proposed LDC-OFDM to MIMO systems, called MIMO-LDC-OFDM, is made in which a single LDC-OFDM codeword is mapped to one transmit antenna. This paper discusses diversity properties of these STF block based designs. An error union bound analysis provides further insights, including more restrictive LDC code design criteria. Compared to other methods of similar complexity, simulations reveal that the bit error rate (BER) performance of full-rate DLD-STFC offers superior performance.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".