Bit Error Probability and Computational Complexity of Bi-directonal Fano Multiple Symbol Differential Detectors
Bibliographic record
Abstract
We present in the paper a suboptimal multiple-symbol differential detector (MSDD) for differential PSK (DPSK) in Rayleigh flat fading channel that uses the Fano Algorithm as its decoding engine. In contrast to a conventional Fano decoder that only searches forward in time for the most promising transmitted pattern, our Fano decoder searches also in the reverse direction, thus providing a mechanism for error detection and correction. The resultant detector, termed a Bi-Fano MSDD, is capable of delivering excellent error performance at moderate implementation complexity over a wide range of signal-to-noise ration (SNR) and fading rates. As an example, for DQPSK modulation and a Doppler frequency of 3 percent the symbol rate, our Bi-Fano MSDD attains almost the same bit-error probability (BEP) performance as the sphere decoder (an efficient implementation of the optimal MSDD) and there is no noticeable irreducible error floor. The most interesting thing is that the computational complexity of the Bi-Fano MSDD is a very stable function of the SNR. In contrast, the sphere decoder has a complexity that grows exponentially as the SNR decreases. In conclusion, the Bi-Fano MSDD incorporates the desirable attributes of the sphere decoder and the DF-DD into one single embodiment. This is consistent with the observation that the Fano decoder is essentially an intelligent DF-DD that uses the accumulated path metric and a running threshold to guide its movement along the decoding tree
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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 | 0.011 |
| 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 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".