Bibliographic record
Abstract
A system level comparison between a conventional OFDM(orthogonal frequency division multiplexing) and the Multi-Symbol Encapsulated (MSE)-OFDM system is presented in this study. An analysis on impact of frequency offset on the system performance is presented. Comparisons are made on two different assumptions, i.e., either keeping the symbol size of the MSE-OFDM (i.e., number of the subcarriers) unchanged to increase the bandwidth efficiency, or keeping the bandwidth efficiency unchanged (ratio between CP(cyclic prefix) and useful data transmission time) for system robustness to synchronization errors, i.e., frequency offset. In the first case for CP-reduced MSE-OFDM, bandwidth efficiency is improved due to a reduced number of CPs inserted between OFDM symbols. For the latter case of FFT size-reduced MSE-OFDM, robustness to synchronization errors is improved considerably due to the smaller number of subcarriers. The proposed system is of particular interest for fixed wireless systems and digital subscriber loops (DSL). Large frame size can be used in these applications, due to the static nature of the channel conditions. Implementation complexity of the MSE-OFDM system is also discussed.
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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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".