Access Point-User Association and Auction Algorithm-Based Pilot Assignment Schemes for Cell-Free Massive MIMO Systems
Bibliographic record
Abstract
In this article, two effective schemes are proposed for association and pilot assignment to tackle the problem of spectral efficiency (SE) maximization and interference management in a user-centric cell-free (CF) massive multiple-input–multiple-output (mMIMO) system. First, a novel access point (AP)–user equipment (UE) association scheme is proposed, which ensures both scalability and association for all the UEs in a CF mMIMO system. After that, a pilot assignment scheme is also proposed which has an iterative structure that utilizes the auction algorithm to solve symmetrical assignment problems for pilot allocation. Numerical results demonstrate the superior performance of the proposed AP–UE association scheme compared to the existing AP preference-based association scheme. Moreover, the proposed pilot assignment scheme provides up to 30% average SE performance and up to 50% reduction in the average interference to signal power ratio (ISR) over that of several competing alternatives. Numerical simulated results further reveal that the proposed pilot assignment scheme improves the average SE and ISR reduction performance regardless of the number of UEs, APs, and pilot sequences in the system. Finally, the proposed pilot assignment scheme manifests the best balance in the tradeoff among the considered performance criteria relative to its comparatives.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 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".