Bibliographic record
Abstract
As wireless channels are fading and error-prone in nature, the adaptive modulation and coding (AMC) scheme is important in wireless communication systems to enhance reliability and spectral efficiency. By adapting transmission schemes to time-varying channels conditions, AMC can provide attractive rate and error performance characteristics. AMC has been widely adopted in the wireless standards, such as GSM and CDMA cellular systems, IEEE 802.11 WLANs, IEEE 802.16 WMANs and also the WPANs based on the short-range ultra-wideband (UWB) systems like the multi-band orthogonal frequency division multiplexing (MB-OFDM) and millimeter wave (MMW). On the other hand, the automated repeat request (ARQ) scheme is typically used as the link-layer error-control mechanism. By retransmitting the corrupted packets, ARQ can further improve the reliability of wireless systems. The interaction of the queueing and ARQ in the link layer with AMC in the PHY layer provides interesting cross-layer design problems. The AMC adopted in the conventional narrowband systems over flat-fading channels (e.g., Rayleigh and Nakagami- m fading) has been studied extensively in the literature [1–4]. There has also been considerable interest in the design and analysis of joint AMC and ARQ transmission systems [5–8]. However, the performance of AMC in short-range high-rate systems, considering the UWB channel characteristics and media access control (MAC) protocols, is much less explored. This chapter is intended to fill this gap by presenting a detailed study of the error-control mechanisms employed in high-rate WPANs.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.011 | 0.005 |
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".