RSMA Precoding Design Based on Interference Nulling and Sum Rate Upper Bound
Bibliographic record
Abstract
Rate splitting multiple access (RSMA), a recent generalization and merging of spatial division multiple access (SDMA) and superposition coding has exponential complexity. Hierarchical streams that transmit a subset of possible streams of encoded messages are proposed for rate splitting and analyzed. To reduce precoding complexity, RSMA with interference nulling (RSMA-IN), that nulls the portion of interference that is not decoded is investigated by formulating a sum rate maximization problem subject to a total power constraint. To solve this non-convex problem, a method that combines interference nulling and stream SNR maximization is proposed. A convex upper bound problem is formulated for the sum rate to compare existing algorithms, and conditions under which the upper bound problem is tight are determined. Taking advantage of the tight cases, we propose an upper bound aided (UBA) enhancement of existing precoding designs by exploiting optimal solutions in the tight cases. It is also shown that the existing augmented weighted mean square error (AWMSE) algorithms cannot converge to a local optimum in sum rate maximization. Simulation results indicate that the proposed approaches enhance performance over existing ones as well as approach their upper bounds in certain instances.
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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 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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 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".