Downlink Resource Allocation in Multiuser Cell-free MIMO Networks with\n User-centric Clustering
Bibliographic record
Abstract
In this paper, we optimize user scheduling, power allocation and beamforming\nin distributed multiple-input multiple-output (MIMO) networks implementing\nuser-centric clustering. We study both the coherent and non-coherent\ntransmission modes, formulating a weighted sum rate maximization problem for\neach; finding the optimal solution to these problems is known to be NP-hard. We\nuse tools from fractional programming, block coordinate descent, and\ncompressive sensing to construct an algorithm that optimizes the beamforming\nweights and user scheduling and converges in a smooth non-decreasing pattern.\nChannel state information (CSI) being crucial for optimization, we highlight\nthe importance of employing a low-overhead pilot assignment policy for\nscheduling problems. In this regard, we use a variant of hierarchical\nagglomerative clustering, which provides a suboptimal, but feasible, pilot\nassignment scheme; for our cell-free case, we formulate an area-based pilot\nreuse factor. Our results show that our scheme provides large gains in the\nlong-term network sum spectral efficiency compared to benchmark schemes such as\nzero-forcing and conjugate beamforming (with round-robin scheduling)\nrespectively. Furthermore, the results show the superiority of coherent\ntransmission compared to the non-coherent mode under ideal and imperfect CSI\nfor the area-based pilot-reuse factors we consider.\n
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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 | 0.002 |
| 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 | 0.000 |
| 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".