Robust Downlink Transmission: An Offset-Based Single-Rate-Splitting Approach
Bibliographic record
Abstract
We consider a multi-user multiple-input single-output downlink system that provides each user with a prespecified level of quality-of-service. The rate-splitting (RS) approach to this problem involves splitting the messages of each user into common and private portions that are transmitted in superposition and decoded sequentially. By adjusting the rates of each portion, the RS approach is able to mitigate the interference that conventional linear beamforming (CLB) schemes incur when users have channels that are closely aligned. However, the transmitter design problem for the RS approach can be quite challenging to solve. In this paper we develop a single-rate-splitting approach, in which RS is applied only to the user with the channel that is “most aligned” with the other channels. This approach greatly reduces the computational cost of RS designs, and admits an offset-based variant that provides robustness to channel estimation errors. Despite its simplifications, our simulation results indicate that the proposed approach retains most of the performance advantage of RS transmission over CLB.
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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 | 0.002 |
| 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) | 0.002 | 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".