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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".