Fractional Programming-Based Uplink Transmission Power Allocation for User-Centric Cell-Free Massive MIMO Systems
Bibliographic record
Abstract
In this paper, two centralized power allocation schemes are proposed for data transmission during the uplink phase in the user-centric cell-free (CF) massive multiple-input multiple-output (mMIMO) systems. The proposed schemes solve two non-convex power allocation problems of maximizing the summation of spectral efficiency (SE) (max-sum-SE) and that of maximizing the minimum SE (max-min-SE) to improve the overall SE and fairness performance while simultaneously reducing the per-user equipment (UE) transmission power. To solve the max-sum-SE problem, we utilize the fractional programming (FP) method to transform the non-convex problem into a series of convex problems. Furthermore, the max-min-SE problem is solved after reformulating it with the help of the FP method along with the alternating direction method of multipliers (ADMM) technique. The proposed schemes are computationally efficient as they solve the aforementioned problems iteratively by using only closed-form updates for the decision variables, which is one of their strongest features, and suitable for allocating power in large-scale CF mMIMO systems. Numerical results demonstrate that, compared to the no power control scheme, the proposed schemes improve the average SE performance by up to 47% while reducing the average transmission power by up to 95%.
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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".