{"id":"W4393078849","doi":"10.1109/twc.2024.3378186","title":"Meta-Learning-Based Fronthaul Compression for Cloud Radio Access Networks","year":2024,"lang":"en","type":"article","venue":"IEEE Transactions on Wireless Communications","topic":"Advanced MIMO Systems Optimization","field":"Engineering","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"Canada Research Chairs","keywords":"Cloud computing; Computer science; Remote radio head; Radio access network; Computer network; Wireless; Telecommunications; Cognitive radio; Base station","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":[0.0005744745,0.0007591958,0.0008246361,0.0002931053,0.0003295332,0.000627203,0.001077018,0.0007054167,0.001096142],"category_scores_gemma":[0.001415759,0.0002587805,0.0003522945,0.0005006355,0.0006186008,0.001045406,0.0006567191,0.001118349,0.000197001],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007245979,"about_ca_system_score_gemma":0.000711876,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004420199,"about_ca_topic_score_gemma":0.004054742,"domain_scores_codex":[0.9997508,0.00005672218,0.00001072865,0.00004479339,0.00007505259,0.00006191097],"domain_scores_gemma":[0.9995745,0.0002365086,0.00005032602,0.00004562779,0.00007226325,0.00002086034],"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":[0.00008748985,0.00004990258,0.0002389269,0.00003698055,0.00002187576,0.00005160533,0.00002777428,0.9378124,0.002698878,0.004276756,0.0005918485,0.05410553],"study_design_scores_gemma":[0.000002205386,0.00001396619,0.0000218216,0.000001590931,0.000002415271,0.000004973812,0.000002367004,0.9985103,0.0005251224,0.0008467588,0.00006713448,0.000001274524],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.04875917,0.0008921691,0.946847,0.0002471068,0.00005137446,0.00003601521,0.0000542205,0.0004179537,0.002695129],"genre_scores_gemma":[0.9464086,0.0003019924,0.05115328,0.000113267,0.00004570094,0.00004698534,0.00007362929,0.00002968586,0.001826928],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.004420199,"threshold_uncertainty_score":0.008788943,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04875405553978786,"score_gpt":0.2948889474121952,"score_spread":0.2461348918724073,"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."}}