{"id":"W4415626547","doi":"10.1109/tccn.2025.3626373","title":"Two-Stage GNN-Based Scalable Access Mode Selection and Power Control for Cell-Free and D2D Heterogeneous Networks","year":2025,"lang":"","type":"article","venue":"IEEE Transactions on Cognitive Communications and Networking","topic":"Advanced MIMO Systems Optimization","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"National Natural Science Foundation of China","keywords":"Heterogeneous network; Scalability; Power control; Network architecture; Selection algorithm; Interference (communication); Access network; Exploit; Enhanced Data Rates for GSM Evolution","routes":{"ca_aff":true,"ca_fund":false,"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":[0.0005187501,0.000570627,0.0004266688,0.0003087705,0.0003189876,0.0004531443,0.001205064,0.0005591724,0.0009685613],"category_scores_gemma":[0.001172815,0.0002535907,0.0002898752,0.0003537558,0.0005154244,0.0007481107,0.0008464201,0.00061988,0.0001389873],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001032842,"about_ca_system_score_gemma":0.0007127897,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.006378893,"about_ca_topic_score_gemma":0.009499302,"domain_scores_codex":[0.9997463,0.00006300439,0.00001001937,0.00006721239,0.00005885157,0.0000546558],"domain_scores_gemma":[0.9996768,0.0001536723,0.00004760876,0.00002742562,0.00006868469,0.00002572597],"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":[0.00006629492,0.00004342394,0.0005462325,0.00002700124,0.00001848998,0.00005896831,0.00003269725,0.939593,0.003233981,0.004739414,0.0007373018,0.05090321],"study_design_scores_gemma":[0.000002550475,0.000008069598,0.0000387802,7.686252e-7,0.000001984101,0.000006957921,0.000002106304,0.9987626,0.0003509462,0.0007530485,0.00007081421,0.000001407977],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.03446212,0.000137272,0.9627789,0.00013438,0.00002772972,0.00004136699,0.00002809737,0.0003013337,0.002088764],"genre_scores_gemma":[0.8865651,0.00009501118,0.110751,0.0001352852,0.00002210352,0.00008226611,0.00007645146,0.00003321953,0.002239659],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.006378893,"threshold_uncertainty_score":0.01268351,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01964589925154482,"score_gpt":0.2888653940990674,"score_spread":0.2692194948475226,"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."}}