Fairness-Aware Pattern-Based Multiple Access for Multi-Group Multicast Systems
Bibliographic record
Abstract
In this paper, we propose pattern-based multicast transmission in a multi-group environment with fairness-aware resource allocation. Traditionally, pattern division multiple access (PDMA) determines the allocation of available radio resources to individual users. In the proposed work, we develop a scheme to assign unique patterns or signatures to multicast groups sharing the same radio resources. When allocating resources to a group, we deal with channel state information (CSI) for all users within the multicast group as to optimize the fairness parameter representing the achievable data rates of different users. In our scheme, to differentiate signals from various multicast groups, we consider resource blocks spanning multiple aspects of signal separation. Within this paper, we demonstrate the principles of the proposed scheme by working with code and frequency allocation. Specifically, PDMA is developed to improve the throughput for each multicast group while maximizing fairness among the groups. Simulation results demonstrate that the proposed PDMA has comparable performance to that of orthogonal frequency-division multiple access (OFDMA) multicasting in terms of aggregate data rate. However, PDMA outperforms OFDMA in terms of inter-group fairness, which makes it a suitable candidate for multi-group multicast transmissions.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 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".