Low-complexity parallel-structure symbol-by-symbol detection for ISI channels
Bibliographic record
Abstract
The problem of practical realization of the optimal fixed-delay symbol-by-symbol detection algorithm is investigated. A new structure is developed which is fully parallel with a speedup factor of 2/sup D+1/ (compared to serial implementation), where D is the delay constraint. Through systematic reformulations of the algorithm, a number of simplifications are performed that avoid the complex and slow computations of exponentials and a large number of multiplications, all at the expense of a small lookup table and a number of simple operations involving additions, comparisons, and table lookups. A number of approximations are applied to this simplified parallel symbol (SPS) detector which lead to the derivation of suboptimal detectors. An interesting suboptimal detector so derived is identical in computations to the Viterbi detector. A comparison of the SPS detector and Viterbi detector shows that the former has a slightly better performance at low values of signal-to-noise ratio (SNR) and the latter has a lower complexity at higher values of SNR; otherwise, the two detectors are comparable in performance and complexity.>
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.004 | 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 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".