Adaptive Modulation for Non-Orthogonal Multiple Access (NOMA) With Imperfect SIC
Bibliographic record
Abstract
The increasing demand for high-capacity wireless communications has always been a challenge for researchers. Non-Orthogonal Multiple Access (NOMA) is one of the emerging schemes that can improve the capacity of wireless networks. In the power-domain NOMA, users share the same frequency and time but with different power levels. In this paper, we propose a novel adaptive modulation scheme for a NOMA-based wireless network to achieve better throughput efficiency and improve the system spectral efficiency by considering imperfect Successive Interference Cancellation (SIC). This scheme allocates the appropriate modulation orders by solving an optimization problem constrained by a maximum Bit Error Rate (BER). We investigate the BER and throughput calculations to assess the efficacy of the proposed scheme. The results show that our adaptive scheme greatly improves the spectral efficiency compared to the traditional fixed modulation NOMA systems, indicating its potential to satisfy the demanding performance standards of future wireless networks.
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".