Rate-Splitting Multiple Access With Finite-Alphabet Constellations: Precoder Optimization and Achievable Rate Performance
Bibliographic record
Abstract
Rate Splitting Multiple Access (RSMA) precoder design with the practical finite-alphabet constellations instead of Gaussian inputs has been addressed in this paper. Considering a multiuser (MU) multiple-input single-output (MISO) broadcast channel (BC) system, we derive a generalized expression of the achievable rate for each user, in a way that the derived expression is generically applicable, e.g., for both underloaded and overloaded cases. Building upon the achievable rate expression, we formulate a multi-objective problem that maximizes the weighted sum rate (WSR) of the considered system, which incorporates with the optimization of the RS precoder for both common and private symbol streams in RSMA. The emphasis here is that our derivation of the achievable rate expression, the problem formulation of the WSR and the optimization of the RSMA precoder all involve the finite alphabet constellation constraint. An iterative gradient descent algorithm with alternative optimization and line search methods is applied to solve the optimization problem. Numerical results show that RSMA can reach the maximum achievable WSR, under both underloaded and overloaded scenarios, with less transmit power compared to the traditional schemes, e.g., space division multiple access (SDMA) and power-domain non-orthogonal multiple access (NOMA). Moreover, thanks to its flexibility, RSMA subsumes both SDMA and NOMA as its subset to fit into different scenarios such as underloaded and overloaded cases with different constellation sizes.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 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 | 0.000 |
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 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".