Bibliographic record
Abstract
The block-spreading technique allows OFDM-based communications systems to exploit frequency diversity. Previous detection algorithms for block-spread OFDM (BSOFDM) are not robust to fast fading, when the communications channel state changes significantly during an OFDM symbol period. It is demonstrated through the use of a new matched filter bound that the performance degradation under fast fading of these algorithms is caused by limitations of the decoding algorithms, and is not an inherent limitation of BSOFDM. An iterative algorithm is introduced with intrinsic data-level parallelism for detecting BSOFDM in the presence of fast fading. Detection is first performed independently on sub-blocks of the received symbol vector. Information is exchanged between these parallel detectors in an iterative manner by estimating the interference between the blocks and removing it from the signal vector. It is shown that the computational complexity of this algorithm is not significantly higher than the detection algorithms for BSOFDM over stationary channels, and achieves excellent BER for fast fading with fixed-point arithmetic, making it suitable for use on embedded systems.
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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 | 0.003 |
| 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 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".