{"id":"W4388430578","doi":"10.1109/twc.2023.3328496","title":"Channel Estimation for Dynamic Metasurface Antennas","year":2023,"lang":"en","type":"article","venue":"IEEE Transactions on Wireless Communications","topic":"Antenna Design and Analysis","field":"Engineering","cited_by":25,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Channel (broadcasting); MIMO; Wireless; Minimum mean square error; Throughput; Control channel; Algorithm; Base station; Electronic engineering; Telecommunications; Mathematics; Engineering; Statistics","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":[0.0003774691,0.000334953,0.0004187344,0.0001597764,0.0001838237,0.00046316,0.0003981909,0.0005747805,0.0009785766],"category_scores_gemma":[0.001192027,0.000204621,0.0003337929,0.0002407597,0.0005013926,0.0006215353,0.0005490873,0.0005877135,0.0001480475],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000384568,"about_ca_system_score_gemma":0.0006739746,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002799958,"about_ca_topic_score_gemma":0.002452197,"domain_scores_codex":[0.9998097,0.00005256691,0.000006608523,0.00004231328,0.0000594534,0.0000293775],"domain_scores_gemma":[0.9995834,0.0002625787,0.00004311989,0.00003858643,0.00005890453,0.00001346479],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.00007946357,0.00001881132,0.0005631687,0.00004138627,0.00001632219,0.0000555285,0.00004197307,0.9595473,0.008361485,0.007115251,0.0003539494,0.02380545],"study_design_scores_gemma":[0.000003520182,0.000008015155,0.00006450984,0.000001374979,0.000001242947,0.00000781745,0.000005607126,0.9983861,0.0006585559,0.0007700418,0.00009082972,0.000002298962],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.05009706,0.0001659805,0.9477437,0.0001977668,0.00002172432,0.0000118311,0.00003294432,0.0001520208,0.001576899],"genre_scores_gemma":[0.9242445,0.0001749229,0.07376324,0.00005444675,0.00002170473,0.00002861352,0.00006520949,0.0000187811,0.001628623],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002799958,"threshold_uncertainty_score":0.005567253,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03479069672247971,"score_gpt":0.277168595993517,"score_spread":0.2423778992710373,"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."}}