{"id":"W4388108199","doi":"10.18280/ts.400514","title":"Hybrid Deep Learning Approach for 6G MIMO Channel Estimation and Interference Alignment HetNet Environments","year":2023,"lang":"en","type":"article","venue":"Traitement du signal","topic":"Advanced MIMO Systems Optimization","field":"Engineering","cited_by":6,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"MIMO; Computer science; Interference (communication); Heterogeneous network; Channel (broadcasting); Electronic engineering; Computer network; Wireless; Telecommunications; Wireless network; Engineering","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005966747,0.0007374679,0.0007339547,0.0004369123,0.00031555,0.0006537087,0.001039952,0.001077936,0.001688007],"category_scores_gemma":[0.0009490792,0.0003858246,0.0005246568,0.0005108831,0.0005335949,0.0007465083,0.0009912963,0.001219428,0.0003393886],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007382062,"about_ca_system_score_gemma":0.001097697,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0100393,"about_ca_topic_score_gemma":0.01172472,"domain_scores_codex":[0.9997353,0.00007386278,0.000009884284,0.00005643313,0.00005913515,0.00006533977],"domain_scores_gemma":[0.9996786,0.0001500838,0.00003416077,0.00002296116,0.00008748744,0.00002663871],"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":[0.00005240949,0.00005468112,0.0007188623,0.00002904258,0.00005530964,0.00006841759,0.00003371625,0.9310431,0.001448957,0.006165373,0.001009886,0.05932019],"study_design_scores_gemma":[0.000001254297,0.00000532381,0.00004026355,0.000001151039,0.000002280224,0.000003620096,0.000002218401,0.9989752,0.000149416,0.0007151045,0.0001028692,0.000001360257],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01520218,0.0002364941,0.9821627,0.0001834176,0.00003240274,0.0000147511,0.00004975934,0.0003278549,0.001790489],"genre_scores_gemma":[0.8051766,0.0004021793,0.1836941,0.0003751812,0.0001117171,0.000119053,0.0003762909,0.0000894605,0.009655435],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.0100393,"threshold_uncertainty_score":0.01996171,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01539117517492197,"score_gpt":0.2163756370095155,"score_spread":0.2009844618345935,"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."}}