Fano space-time multiple symbol differential detectors
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
We present in the paper a multiple-symbol differential detector (MSDD) for differential space-time (ST) codes in Rayleigh flat fading channel. The detector, termed a Fano ST-MSDD, uses the Fano algorithm as its decoding engine and is capable of delivering excellent error performance at moderate implementation complexity over a wide range of fading rates. Essentially, the detector is an "intelligent" decision feedback detector (DFD) that uses a running threshold and the accumulated path metric as navigation tools when it roams the decoding tree. In the static channel, our best Fano ST-MSDD scheme with a detection window size of N=6 is able to narrow the original 3 dB gap between ideal coherent and conventional ST differential detection by 1 dB. In a fast fading channel with a Doppler frequency of three percent the ST symbol rate, the error curve of our best TV-10 Fano ST-MSDD is able to "track" that of the ideal coherent detector (with a 3-dB gap in between) and there is no irreducible error floor. All these performances become even more remarkable when we consider the rather moderate implementation complexity reported in the paper. Because of its close relationship with a DFD, the Fano ST-MSDD has a similar complexity as the DFD at large signal-to-noise ratio (SNR). Actually, we found that the complexity of the Fano ST-MSDD is a relatively stable function of the SNR
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.
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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.003 | 0.001 |
| 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 it