Adaptive Subcarrier Allocation for Orthogonal Frequency Code Division Multiplexing (OFCDM)
Bibliographic record
Abstract
In this thesis, a novel adaptive subcarrier allocation algorithm is developed for OFCDM. This algorithm utilizes groups of evenly spaced, non-contiguous subcarriers throughout the spectrum. The users are allocated to subcarriers groups with the intention of minimizing the overall BER. This is accomplished by assigning a user to the set of subcarriers that provides the best Signal to Interfenence and Noise Ratio (SINR), while producing the least amount of interference to other users accessing the same subcarrier group. The expected interference produced by this user is then utilized to recalculate the SINR for the subcarrier goup, and the allocation process continues recursively until all users are assigned to subcarriers. The proposed alogrithm is shown to provide a performance improvement ranging from 1.5dB with 2x16 spreading, 7dB with 16x2 spreading. The algorithm is also shown to maintain or improve the BER floor for each OFCDM spreading configuration.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".