Hybrid NOMA in Multi-Cell Networks: From a Centralized Analysis to Practical Schemes
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Bibliographic record
Abstract
We investigate the performance of a hybrid non-orthogonal multiple access (NOMA) multi-cell downlink system (called hybrid as different users can have different successive interference cancellation (SIC) capabilities) by first formulating and solving a centralized proportional fair scheduling genie-assisted problem that jointly performs user selection, power allocation, power distribution, and modulation and coding scheme (MCS) selection. While such a genie is practically infeasible, it upper bounds the achievable performance. The results indicate that hybrid NOMA with a maximum of 2 multiplexed users can bring significant gains over a traditional OMA system (as long as enough users have the maximum SIC capability). Additionally, results show that the simple equal power allocation scheme (often used in the literature) yields performance lower than half the upper bound. Thus, we propose a simple static coordinated power allocation scheme across all cells for NOMA using a simple power map that is easily calibrated offline and show that with the calibrated power map, performance improves by 80%. Finally, we focus on the online scenario and propose a family of practical scheduling algorithms, each of them exhibiting a different trade-off between complexity (i.e., run-time) and performance.
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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 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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 it