Hybrid NOMA in Multi-Cell Networks: From a Centralized Analysis to Practical Schemes
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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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".