Weighted Sum-Rate Maximization for Distributed RIS-Assisted Cell-Free Massive MIMO
Bibliographic record
Abstract
The feasibility of utilizing distributed reconfigurable intelligent surfaces (RISs) in a cell-free massive multiple-input multiple-output (mMIMO) setup is investigated. To maximize the system-wide weighted sum rate under the minimum quality-of-service of the users and a transmit power constraint at the access points (APs), we formulate a joint optimization problem for the transmit/active beamformers at the APs and reflective/passive beamforming at the distributed RISs. We solve this optimization problem by using a fractional programming-based alternate optimization method. We present a comprehensive set of numerical results to evaluate the performance of the proposed system model and optimization framework. Moreover, the proposed algorithm’s convergence and computational complexity aspects, and our analytical and simulation results investigate the effects of the numbers of APs, RISs, and reflecting elements. Consequently, we reveal that the proposed distributed RIS-assisted cell-free mMIMO system helps to achieve high spectral efficiency gains for future wireless networks.
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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) | 0.001 | 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) | 0.001 | 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".