A VLSI implementation of an adaptive-effort low-power Viterbi decoder for wireless communications
Bibliographic record
Abstract
Low-power error-correction is required for 3rd generation digital wireless devices. Adaptive-reduced state sequence detection (A-RSSD) modifies a Viterbi decoder to use far less computational effort than is typical. RSSD neglects the oldest p bits of the encoder's state machine, treating the code as if it were of length K/spl acute/=K-p. Through successive reduction of p, decoding can proceed with more effort until a frame is correctly decoded. This paper describes the only known VLSI implementation of A-RSSD. The presented architecture is an adaptive strength, state-parallel, bit-serial structure. It features soft-decision, continuous stream traceback decoding, with K' ranging from 3 to 11. As such it employs between 4 and 1024 ACS units. The branch metric computer and ACS units are mostly conventional, while special consideration must be given to branch label generation, sub-state estimation, and ACS interconnection structure. Other low-power techniques are also applied, specifically with respect to clock gating, and traceback RAM structure. Design tradeoffs are discussed, and performance estimates are presented.
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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 | 0.000 | 0.001 |
| 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 | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".