Achievable Splitting and Decoding in the Multiple Access Channel: A Hierarchical Approach
Bibliographic record
Abstract
Rate-splitting improves fairness, sum rate, and outage performance of additive white Gaussian noise (AWGN) single-input single-output (SISO) multiple-access channels (MAC) with low complexity and low latency. Rate-splitting requires neither time sharing nor joint encoding and decoding, making it suitable for time-sensitive applications. Although a rate-splitting scheme is proven to exist for any rate tuple in the rate region, an explicit MAC rate-splitting scheme has not been reported in the literature to date. Previously proposed approaches considered rate-splitting parameters as variables in sum rate and fairness optimization, and these previous formulations require intractable integer programming. In this paper, the problem of maximizing the sum rate and fairness is first formulated independently of the rate-splitting method. Then, a low complexity algorithm is proposed to obtain the rate-splitting scheme to achieve a given rate tuple. The algorithm can be extended to single-input-multiple-output (SIMO) MAC with known channels by adding minimum mean square error (MMSE) receivers for each virtual user. Examples are provided to illustrate the processing steps and complexity of the proposed algorithm.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.002 |
| 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".