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.
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 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 | 0.001 | 0.000 |
| 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 | 0.001 | 0.001 |
| Open science | 0.004 | 0.006 |
| 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".