{"id":"W4394828369","doi":"10.1109/comst.2024.3388511","title":"Evolution of RAN Architectures Toward 6G: Motivation, Development, and Enabling Technologies","year":2024,"lang":"en","type":"article","venue":"IEEE Communications Surveys & Tutorials","topic":"Advanced Wireless Communication Technologies","field":"Engineering","cited_by":59,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"National Key Research and Development Program of China; Innovation and Entrepreneurship Talent Project of Lanzhou; Natural Sciences and Engineering Research Council of Canada; National Natural Science Foundation of China","keywords":"Ran; Computer science; Cognitive science; Psychology; Computer network","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001305224,0.0002117222,0.0003086133,0.0005576398,0.0001937778,0.00006644321,0.001228387,0.0002021147,0.000003283125],"category_scores_gemma":[0.00112308,0.0002146842,0.00004521289,0.000883463,0.0004912242,0.0002085665,0.0003717988,0.0004041906,0.00000842835],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002017836,"about_ca_system_score_gemma":0.0000858496,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00003001106,"about_ca_topic_score_gemma":0.00005369409,"domain_scores_codex":[0.9984867,0.0003033324,0.0006221956,0.0002094823,0.0001647501,0.000213525],"domain_scores_gemma":[0.9961051,0.001813305,0.00009606292,0.001805378,0.0001559798,0.00002412488],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.000009425933,0.0001063855,0.003017616,0.0007932447,0.0004073314,0.000001453075,0.006316667,0.0294448,0.1706611,0.06503265,0.0005256874,0.7236837],"study_design_scores_gemma":[0.0007981401,0.00007224758,0.01264928,0.001232205,0.00007747793,0.00002401763,0.00709344,0.01996542,0.763454,0.06258233,0.1305132,0.001538272],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3304476,0.08283803,0.573525,0.0007109078,0.001304334,0.00072856,0.00006464805,0.00941594,0.0009650268],"genre_scores_gemma":[0.9612076,0.004356325,0.03414818,0.000001696359,0.00003285432,0.0001561196,0.00003183564,0.00004268016,0.00002268353],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.7221454,"threshold_uncertainty_score":0.8754568,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05064797454356461,"score_gpt":0.2831716751143451,"score_spread":0.2325237005707805,"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."}}