Cooperative Rate Splitting Multiple Access in Multi-Cell Networks
Bibliographic record
Abstract
This paper explores downlink Cooperative Rate-Splitting Multiple Access (C-RSMA) in a multi-cell wireless network with the assistance of Joint-Transmission Coordinated Multipoint (JT-CoMP). In this network, each cell consists of a base station (BS) equipped with multiple antennas, a cell-center user (CCU), and a cell-edge user (CEU) located at the edge of adjacent cells. Through JT-CoMP, all BSs collaborate to simultaneously transmit the data to all users including the CCUs and CEU. To enhance the signal quality for the CEU, CCUs relay the common stream to the CEU by operating in half-duplex (HD) relaying mode. We aim to jointly optimize the beamforming vectors at the BS, the allocation of common stream rates, the transmit power at relaying users, i.e., CCU s, and the time slot fraction aiming to maximize the minimum achievable data rate. The formulated problem is non-convex and challenging to solve directly. To address this, we employ change-of-variables, first-order Taylor approximations and a low-complexity algorithm based on Successive Convex Approximation (SCA). We demonstrate the efficacy of the proposed scheme, in terms of average achievable data rate, and we compare its performance to that of four baseline schemes, including HD cooperative non-orthogonal multiple access (C-NOMA), NOMA, and RSMA without user cooperation. The results show improvements of 12% and 41 % over RSMA and HD C-NOMA, respectively in high channel disparity between the BS and UEs.
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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) | 0.000 | 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".