{"id":"W4410852322","doi":"10.1109/comst.2025.3574765","title":"A Comprehensive Survey of Knowledge-Driven Deep Learning for Intelligent Wireless Network Optimization in 6G","year":2025,"lang":"en","type":"article","venue":"IEEE Communications Surveys & Tutorials","topic":"Wireless Body Area Networks","field":"Engineering","cited_by":33,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"National Natural Science Foundation of China","keywords":"Computer science; Wireless network; Deep learning; Artificial intelligence; Wireless; Data science; Telecommunications","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":[0.000962329,0.001072963,0.0009982055,0.001214678,0.0003041684,0.001605511,0.001300171,0.001104893,0.003727146],"category_scores_gemma":[0.002202949,0.0004840262,0.0007021065,0.002275758,0.0004052449,0.00255378,0.001090678,0.001559629,0.001222171],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008633067,"about_ca_system_score_gemma":0.001246347,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002321943,"about_ca_topic_score_gemma":0.002217062,"domain_scores_codex":[0.9995172,0.0001256658,0.0000585257,0.00008236831,0.0001724777,0.00004373493],"domain_scores_gemma":[0.9993488,0.0004140787,0.00003249042,0.00004564038,0.0001341026,0.00002478752],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00004932539,0.0001054511,0.0009312078,0.00419584,0.0001317159,0.0001565818,0.0001453612,0.04701227,0.001349175,0.07443211,0.02468505,0.8468059],"study_design_scores_gemma":[0.00002157683,0.0002677561,0.001642822,0.003345105,0.0002049164,0.0006394768,0.0002126794,0.3831697,0.003166213,0.1405911,0.4666426,0.00009614907],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"review","genre_gemma":"review","genre_scores_codex":[0.005723157,0.5063604,0.4483732,0.003895569,0.0008251526,0.0001212211,0.0004812811,0.0004704076,0.03374954],"genre_scores_gemma":[0.08872879,0.7934188,0.1000551,0.00156357,0.002051019,0.0002277389,0.001316807,0.0001456442,0.01249254],"genre_candidate":"review","genre_consensus":"review","teacher_disagreement_score":0.003727146,"threshold_uncertainty_score":0.01246852,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04789955175526732,"score_gpt":0.3072947457759713,"score_spread":0.259395194020704,"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."}}