Non-Orthogonal Multiple Access with Index Modulated Non-Orthogonal Frequency Division Multiplexing
Bibliographic record
Abstract
This work proposes spectral efficient frequency division multiplexing (SEFDM) based non-orthogonal multiple access (NOMA) with subcarrier index modulation (SIM). SEFDM exploits the frequency division multiplexing in a non-orthogonal manner to acquire a spectral efficiency (SE) better than conventional orthogonal frequency division multiplexing (OFDM) at the cost of increased inter-carrier interference. In SEFDM-SIM, the subcarriers are activated as per the incoming bitstream to convey supplemental bits of information virtually through the active subcarrier indices, offering a balance between SE and error performance. Additionally, NOMA allocates varying levels of power for particular users depending on the distance between the users and the base station (BS), which allows to provide service for multiple users on the very resource and increase SE further. The performance of NOMA-SEFDM-SIM is investigated in the context of of bit-error rate and SE.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".