Max-Min Fairness for Beamspace MIMO-NOMA: From Single-Beam to Multi-Beam
Bibliographic record
Abstract
With the help of non-orthogonal multiple access (NOMA), the number of connections of the beamspace multiple-input multiple-output (MIMO) systems can be improved with enhanced sum-rate performance, which constitutes beamspace MIMO-NOMA. Thus, most relevant papers focus on improving the system sum rate, which may inflict unbearable rate loss to weak users. To ensure the achievable rates of weak users, we maximize and analyze the minimal rate of the system in the single-beam case as well as the multi-beam case, where two completely different phenomena are revealed. Particularly, in the single-beam case, the maximized minimal rate of the beamspace MIMO-NOMA always grows rapidly with the signal-to-noise-ratio (SNR), and is larger than that of the beamspace MIMO using orthogonal multiple access (beamspace MIMO-OMA). However, in the multi-beam case, the maximized minimal rate of the beamspace MIMO-NOMA grows slower and slower in the high-SNR region, where it is smaller than that of the beamspace MIMO-OMA. To explain this difference, it is disclosed that the intra-beam interference in the single-beam case is ofsuccessive pattern, which is proved to have no limit on the max-min rate. In contrast, the inter-beam interference in the multi-beam case is ofmutual pattern, which is proved to restrict the max-min rate to a derived upper bound.
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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 | 0.010 | 0.018 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".