Precoding Design and Sum Rate upper bound of RSMA Using Interference Nulling
Bibliographic record
Abstract
Rate-splitting multiple access (RSMA) is a new multiple access technique that improves limitations of conventional multiple access techniques. RSMA manages interference by precoding at the transmitter and successive interference cancellation (SIC) at the receivers. Several precoding design approaches are considered in the literature that propose algorithms to solve a joint optimization problem. In this work, we focus on a special class of RSMA that nulls the interference of streams that must not be decoded, i.e., RSMA with interference nulling (RSMA-IN). We consider a sum rate maximization problem subject to a total power constraint and interference nulling (IN). To address the performance of precoding design algorithms relative to the optimal sum rate of RSMA-IN, we propose a convex upper bound problem for RSMA-IN sum rate and compare its performance with that of the precoding design algorithms. Further, to take advantage of the cases where the upper bound problem achieves the optimal sum rate, we propose an upper bound aided (UBA) algorithm to both enhance the performance and reduce the complexity of existing precoding design algorithms. Simulation results show that the performance of the UBA algorithm is close to the optimal sum rate of RSMA-IN.
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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 | 0.002 | 0.012 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".