Bibliographic record
Abstract
Prior work has developed a variation of MCCDMA, block-spread OFDM (BSOFDM), providing good error performance over stationary channels with low detection cost and allowing parallel computation to reduce the receiver latency. The previous detection algorithms developed for BSOFDM are not robust to fast fading. In this paper, it is demonstrated through the use of performance bounds that the performance degradation under fast fading is caused by limitations of the previous decoding algorithms, and is not an inherent limitation of BSOFDM. A novel iterative detection algorithm is introduced with intrinsic data-level parallelism for detecting BSOFDM in the presence of fast fading. Detection is first performed independently on subblocks of the received block 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 prior detection algorithms for BSOFDM, and achieves excellent BER for fast fading with fixed-point arithmetic, making it suitable for use on embedded systems. In addition the inherent parallelism of this algorithm means multiple computational units can be exploited, if available, to reduce receiver latency.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".