Transmission and Reception Concepts for WLAN IEEE 802.11b
Bibliographic record
Abstract
State-of-the-art wireless local area network (WLAN) IEEE 802.11b terminals employ complementary code keying (CCK) as modulation format. In this paper, receiver concepts tailored for CCK transmission over frequency-selective fading channels are presented in a unified and systematic framework. First, optimum maximum-likelihood (ML) detection for CCK signaling is considered. Second, for complexity reduction, minimum mean-squared error block decision-feedback equalization (MMSE-DFE) is investigated and reduced-state sequence estimation (RSSE) is considered on the basis of an Ungerbock-like set partitioning of the multidimensional CCK code wordset. In order to improve the reliability of CCK transmission over fading channels, time-reversal space-time block codes (TR-STBCs) combined with receive diversity are applied. Simulation results of the considered suboptimum receivers are compared with a performance approximation for optimum detection. Our results demonstrate the excellent performance of the advocated equalization schemes and the significant gains that can be achieved with TR-STBCs and receive diversity in typical WLAN environments
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 |
| 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 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".