A Novel Heap-based Pilot Assignment for Full Duplex Cell-Free Massive MIMO with Zero-Forcing
Why this work is in the frame
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Bibliographic record
Abstract
This paper investigates the combined benefits of full-duplex (FD) and\ncell-free massive multiple-input multipleoutput (CF-mMIMO), where a large\nnumber of distributed access points (APs) having FD capability simultaneously\nserve numerous uplink and downlink user equipments (UEs) on the same\ntime-frequency resources. To enable the incorporation of FD technology in\nCF-mMIMO systems, we propose a novel heapbased pilot assignment algorithm,\nwhich not only can mitigate the effects of pilot contamination but also reduce\nthe involved computational complexity. Then, we formulate a robust design\nproblem for spectral efficiency (SE) maximization in which the power control\nand AP-UE association are jointly optimized, resulting in a difficult\nmixed-integer nonconvex programming. To solve this problem, we derive a more\ntractable problem before developing a very simple iterative algorithm based on\ninner approximation method with polynomial computational complexity. Numerical\nresults show that our proposed methods with realistic parameters significantly\noutperform the existing approaches in terms of the quality of channel estimate\nand SE.\n
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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 it