Uplink Resource Allocation for RSMA-Aided Digital Twin-Assisted User-Centric Cell-Free Massive MIMO Systems
Bibliographic record
Abstract
This paper investigates uplink radio resource optimization of a user-centric (UC) cell-free (CF) massive multiple-input multiple-output (mMIMO) system aided by the rate splitting multiple access (RSMA) technique subject to pilot contamination. We formulate problem to maximize the minimum spectral efficiency (SE) problem by jointly addressing decoding order selection, power allocation, and access point (AP) - user equipment (UE) association assignment. The envisioned optimization exhibits two challenges. First, it requires global channel state information (CSI) for near-optimal performance, which incurs substantial overhead and data collection costs in large-scale CF networks. Second, the optimization is intractable due to its NP-hard and discrete non-linear programming nature. To address the CSI acquisition issue, we utilize a digital twin (DT) of the CF mMIMO system, leveraging its context-awareness to acquire global CSI with reduced overhead. To address computational intractiablity of the optimization problem, we decompose it into three sub-problems. The power allocation sub-problem is transformed into a second-order cone programming problem and solved by the bisection method. Additionally, we propose a computationally efficient heuristic approach for power allocation. Next, we propose an analytical method for the decoding order selection by ranking the channels in descending order of strength. Simulation results validate the ability of the proposed approach to attain the near-optimal performance. Subsequently, the AP-UE association assignment problem is solved by a heuristic approach to further improve the SE performance. Finally, we solve the original NP-hard problem in a unified manner via the block-coordinate descent algorithm. Simulation results underscore a substantial 61% improvement in the SE performance when integrating the RSMA technique into a UC CF mMIMO system.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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 teacher head, 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".