Coordinated Full-Duplex Cooperative Transmission With RSMA in Multi-Cell Networks
Bibliographic record
Abstract
This paper investigates the downlink performance of Cooperative Rate-Splitting Multiple Access (C-RSMA) in a multi-cell wireless network employing Joint-Transmission Coordinated Multipoint (JT-CoMP). Each cell comprises a multi-antenna base station (BS), multiple cell-center users (CCUs), and multiple cell-edge users (CEUs). Leveraging JT-CoMP, all BSs jointly transmit data to both CCUs and CEUs. To enhance signal quality at the cell edges, CCUs assist by relaying the common stream to CEUs via full-duplex (FD) decode-and-forward relaying. We aim to jointly optimize the beamforming vectors at the BSs, the allocation of common stream rates, and the transmit power at relaying users, i.e., CCUs, aiming to maximize the minimum achievable data rate. To address the non-convex challenge, we employ change-of-variables, first-order Taylor approximations, and a low-complexity algorithm based on Successive Convex Approximation (SCA). In this context, we also evaluate our proposed system model considering imperfect channel state information (CSI) and imperfect successive interference cancellation (SIC). The results show that the proposed FD C-RSMA can achieve 25% over FD C-NOMA with BS transmit power of 20 dBm.
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".