Efficient Bit Loading Algorithm for OFDM-NOMA Systems with BER Constraints
Bibliographic record
Abstract
This paper considers applying bit loading to multicarrier non-othogonal multiple access (NOMA) systems. The objective is to maximize the system total throughput while satisfying the users’ individual quality of service (QoS) constraints. Although bit loading is generally an NP-hard problem, even for orthogonal multiple access (OMA), the mutual interference between the users and the dependence of power coefficients and modulation orders are additional challenges that add substantial complexity to the optimization problem. Therefore, we propose in this paper an efficient bit loading algorithm for multicarrier NOMA systems and compares the complexity and throughput with OMA. The obtained results show that NOMA has virtual cognition and hybrid modes of operation, NOMA/OMA, that enables NOMA to outperform OMA by 100% for the two-user scenario. The complexity of the loading process for NOMA is noticeably higher than OMA, which is due to the high computational complexity of bit error rate (BER) computation for NOMA. The obtained results show that the NOMA throughput depends on which user is loaded first, and on the initially loaded bits.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".