Unsupervised Learning for Distributed Downlink Power Allocation in Cell-Free mMIMO Networks
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Bibliographic record
Abstract
Cell-free massive multiple-input multiple-output (CF-mMIMO) surmounts conventional cellular network limitations in terms of coverage, capacity, and interference management. This paper aims to introduce a novel unsupervised learning framework for the downlink (DL) power allocation problem in CF-mMIMO networks, utilizing only large-scale fading (LSF) coefficients as input, rather than the hard-to-obtain exact user location or channel state information (CSI). Both centralized and distributed CF-mMIMO power control learning frameworks are explored, with deep neural networks (DNNs) trained to estimate power coefficients while addressing the constraints of pilot contamination and power budgets. For both learning frameworks, the proposed approach is utilized to maximize three well-known power control objectives under maximum-ratio and regularized zero-forcing precoding schemes: 1) sum of spectral efficiency, 2) the minimum signal-to-interference-plus-noise ratio (SINR) for max-min fairness, and 3) the product of SINRs for proportional fairness, for each of which customized loss functions are formulated. The proposed unsupervised learning approach circumvents the arduous task of training data computations, typically required in supervised learning methods, bypassing the use of conventional complex optimization methods and heuristic methodologies. Furthermore, an LSF-based radio unit (RU) selection algorithm is employed to activate only the contributing RUs, allowing efficient utilization of network resources. Simulation results demonstrate that our proposed unsupervised learning framework outperforms existing supervised learning and heuristic solutions, showcasing an improvement of up to 20% in spectral efficiency and more than 40% in terms of energy efficiency compared to state-of-the-art supervised learning counterparts.
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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) | 0.000 | 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 | 0.001 |
| 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