Low Complexity Rate Splitting Using Hierarchical User Grouping
Bibliographic record
Abstract
Rate splitting multiple access (RSMA) is a powerful multiple access technique that exploits the advantages of both space-division multiple access (SDMA) and nonorthogonal multiple access (NOMA), making it suitable for heterogeneity of quality of service and high throughput requirements of future networks. RSMA is based on rate splitting at the transmitter and successive interference cancellation (SIC) at the receiver. The number of transmitted streams and the number of possible decoding orders grows exponentially with the number of users. In this paper, a low-complexity rate splitting approach is proposed that finds a subset of all possible common streams by hierarchical user grouping and assigning a common stream to each group. Since the hierarchical user grouping approach creates groups of users of the same size that do not have a user in common, this approach does not require user ordering. To implement this low-complexity RSMA, alternating max-min precoding design and power allocation is used. The beamforming vectors are designed to cancel the multiuser interference. As a result, the beamforming vectors can be designed in multiple levels. Simulation results show that the proposed scheme is able to achieve attractive performance vs. complexity tradeoffs compared to several others in the literature.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".