Access Point Clustering in Cell-Free Massive MIMO Using Conventional and Federated Multi-Agent Reinforcement Learning
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Bibliographic record
Abstract
Cell-free massive multiple-input multiple-output (MIMO) systems consist of geographically-distributed multi-antenna access points (APs) that form a virtual massive MIMO array. To make the network arbitrarily scalable in size, each user should be served by the best possible personalized user-centric cluster of nearby APs. Unfortunately, determining that cluster is a combinatorially-complex problem made even harder when the users are in motion. Therefore, in this work, we develop a multi-agent reinforcement learning (MARL) algorithm for AP selection and clustering. Each AP is an agent in the MARL algorithm and it is trained to near-optimally select for itself which users to serve. Conventional MARL algorithms require a centralized reward system to train the agents, and the agents’ neural network weights tend to strongly depend on their locations during training. To counteract these problems, we also consider a federated MARL framework. Simulation results demonstrate both our conventional and federated MARL algorithms outperform existing published AP selection algorithms, and also provide performance comparable to the case of all APs serving all users. The results also show the conventional algorithm has somewhat superior performance in the environment it was trained in, but the federated algorithm transfers its learning to changed environments much better, with very little performance loss.
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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 | 0.001 | 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