Sphere constrained block DFE with per-survivor intra-block processing for CCK transmission over ISI channels
Bibliographic record
Abstract
In the wireless local area network (WLAN) standard IEEE 802.11b, complementary code keying (CCK) modulation has been adopted for the high data rate transmission mode. In this paper, complexity reduction for block decision-feedback equalization (bDFE), tailored for CCK transmission over frequency-selective channels, is considered. Since the CCK signal may be viewed as a linear block code with respect to the chip phases of the codeword, a trellis diagram with a minimum number of states can be designed that represents the properties of the CCK code set. The Viterbi algorithm (VA) with per-survivor processing is applied to the CCK trellis for decoding and accounting for the inter-chip interference, while inter-codeword interference is canceled by decision feedback. The resulting scheme is denoted as bDFE-pS and has a significantly lower complexity than bDFE with brute-force search. By introducing a sphere constraint on the CCK trellis (SC-bDFE-pS), the complexity of bDFE-pS can be further reduced. Omitting trellis states that violate the sphere constraint, edges that emanate from such states can be pruned, and the average number of metric calculations per CCK trellis segment can be reduced. Simulation results show that the performance of bDFE-pS and SC-bDFE-pS, respectively, is essentially equivalent to that of bDFE with brute-force search.
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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 | 0.002 |
| 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 | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".