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.
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 | 0.001 |
| 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 | 0.001 |
| 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".