Robust Beamforming Design for RSMA-Integrated Full-Duplex Communications: Energy and Spectral Efficiency Trade-Off
Bibliographic record
Abstract
In this paper, we investigate an unconventional full-duplex (FD) integrated rate-splitting multiple access (RSMA) scheme for improved spectral efficiency (SE) and energy efficiency (EE) performance when compared to the conventional power-domain schemes. In particular, we focus on improving the energy efficiency (EE) and spectral efficiency (SE) trade-off for the multiple users subject to robust beamforming design and smart inter-user interference mitigation under imperfect channel state information (CSI). We formulate a multi-objective optimization (MOO) problem, specifically aiming to jointly maximize EE and SE within the FD-RSMA system by jointly optimizing the resource allocation subject to the limits on transmit power and minimum rate, under the assumption of a CSI error model with a bound. Initially, the MOO problem is converted into a single objective optimization (SOO) problem using the weighted sum method, with a trade-off parameter. An iterative algorithm is employed, utilizing successive convex approximation and the S-procedure to achieve near-optimal resource allocation for the transformed SOO problem, with a particular emphasis on effective interference management. Simulation results highlight the effectiveness of the FD-RSMA scheme, demonstrating its superiority over the multi-user FD space division multiple access by 16.93 % and non-orthogonal multiple access scheme by 76.04 %.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".