Pilot Power Allocation Scheme for User-Centric Cell-free Massive MIMO Systems
Bibliographic record
Abstract
This paper proposes a pilot power allocation scheme that simultaneously reduces both the pilot contamination effect and per-user pilot transmission power for the user-centric cell-free (CF) massive multiple-input multiple-output (mMIMO) systems during the uplink training phase. The proposed pilot power allocation scheme comprises a simple algorithm that tries to minimize the channel estimation error iteratively for improving the estimation quality. It is found from the simulated results that the average convergence time of the proposed scheme demonstrates a scalable attribute. Moreover, numerical results verify the effectiveness of the proposed scheme in improving the 95%-likely spectral efficiency performance by up to 16% and reducing the average pilot transmission power by up to 78 % compared to a scheme that transmits full pilot power (i.e., no control) and a recent pilot power allocation scheme that uses successive approximation technique with first-order Taylor approximation.
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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".