{"id":"W4379471151","doi":"10.1109/tmlcn.2023.3283228","title":"Access Point Clustering in Cell-Free Massive MIMO Using Conventional and Federated Multi-Agent Reinforcement Learning","year":2023,"lang":"en","type":"article","venue":"IEEE Transactions on Machine Learning in Communications and Networking","topic":"Advanced MIMO Systems Optimization","field":"Engineering","cited_by":37,"is_retracted":false,"has_abstract":true,"ca_institutions":"Huawei Technologies (Canada); University of Alberta","funders":"Natural Sciences and Engineering Research Council of Canada; Huawei Technologies","keywords":"Reinforcement learning; Cluster analysis; Computer science; Reinforcement; Point (geometry); MIMO; Distributed computing; Artificial intelligence; Computer network; Mathematics; Engineering; Structural engineering; Channel (broadcasting)","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001260111,0.0006303642,0.0009015352,0.0003400368,0.0004848849,0.000703787,0.001267722,0.0008681708,0.0005676881],"category_scores_gemma":[0.002781811,0.0003317853,0.0003839146,0.0003026812,0.000967579,0.0009273089,0.001073473,0.0007594523,0.0001476424],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001032308,"about_ca_system_score_gemma":0.001014155,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.006572532,"about_ca_topic_score_gemma":0.00513359,"domain_scores_codex":[0.9994884,0.0001909944,0.00001830453,0.0001052507,0.000100254,0.00009683242],"domain_scores_gemma":[0.9986193,0.0006687987,0.000195988,0.0001218238,0.0002578627,0.0001362305],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00003777943,0.0000285945,0.0003459751,0.000009066708,0.00001315294,0.00003048741,0.00001938114,0.9903817,0.0004240934,0.0012173,0.0001730935,0.007319474],"study_design_scores_gemma":[0.00000416492,0.00001093451,0.00002910383,6.68839e-7,0.000001290425,0.000003612924,0.00000256965,0.9994408,0.00009079154,0.0003808837,0.00003371531,0.000001341844],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1115874,0.0002889892,0.8849536,0.00032572,0.00006240013,0.00006912549,0.00002852895,0.0005432533,0.002141075],"genre_scores_gemma":[0.9572615,0.00005768377,0.04133247,0.0001063055,0.00001748019,0.00004348058,0.00002540094,0.00001738594,0.001138438],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.006572532,"threshold_uncertainty_score":0.01306856,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04482128896893808,"score_gpt":0.294742777721717,"score_spread":0.2499214887527789,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}