Systolic Array-Based Pipelining Design of CCK Demodulators
Bibliographic record
Abstract
Complementary code keying (CCK) is one of the few channel coding techniques used in the widely deployed 802.11b wireless local area networks (WLANs). CCK is used for transmitting 5.5 and 11 Mbps data transfer rates. In this paper, we propose a design to improve the efficiency of CCK demodulation. The proposed systolic array architecture exploits and reuses the replicated butterfly structure of modified fast Walsh transform (MFWT). A typical MFWT CCK demodulator, consists of three stages, accepts all eight chips of the CCK codeword simultaneously, and outputs sixty-four values. Upon deploying the proposed systolic array design with its pipelining nature, instead of processing all eight chips of CCK in parallel during the first stage, every two chips can be processed immediately. At each stage, the results are moved instantly to the next stage upon multiplying the appropriate constants. This resulting systolic array architecture has many advantages over the conventional design. Firstly, a serial-to-parallel converter to convert serial incoming data to parallel blocks of eight chips is not required. This reduces hardware complexity. Secondly, every stage in the architecture is continuously processing data; whereas, in the conventional design, hardware in each stage is left idle after processing until inputs from the next eight chips block arrives. Thirdly, twenty-eight butterflies are needed in a conventional design, but, only thirteen butterflies are required due to hardware reuse in the proposed architecture.
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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 | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".