{"id":"W3131027150","doi":"","title":"Enhanced Channel Tracking in THz Beamspace Massive MIMO: A Deep CNN Approach","year":2020,"lang":"en","type":"article","venue":"Asia-Pacific Signal and Information Processing Association Annual Summit and Conference","topic":"Millimeter-Wave Propagation and Modeling","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University","funders":"","keywords":"Computer science; Convolutional neural network; MIMO; Channel (broadcasting); Overhead (engineering); Deep learning; Artificial intelligence; Noise (video); Signal-to-noise ratio (imaging); A priori and a posteriori; Exploit; Computer engineering; Pattern recognition (psychology); Algorithm; Image (mathematics); 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":[0.0002208652,0.0005642545,0.0003311709,0.000201813,0.0001606339,0.0003838275,0.0007934103,0.0005254861,0.0009972488],"category_scores_gemma":[0.0006216106,0.0002248752,0.0002417697,0.0002698517,0.0002674214,0.0007062588,0.0005984925,0.0007342817,0.0003021236],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004110715,"about_ca_system_score_gemma":0.0004638532,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004738674,"about_ca_topic_score_gemma":0.007476355,"domain_scores_codex":[0.9999324,0.000009204707,0.00000224163,0.00001549102,0.0000220458,0.00001861556],"domain_scores_gemma":[0.9998355,0.00005778386,0.00002225819,0.00002391622,0.00004708092,0.00001339196],"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":[0.0001067953,0.00005637279,0.001302503,0.00006312056,0.00005376878,0.0000993762,0.0000401107,0.8346407,0.0195196,0.00779176,0.001642364,0.1346835],"study_design_scores_gemma":[0.000001277809,0.000008195002,0.00008339139,0.000001665646,0.000003165414,0.00001000491,0.000002241479,0.9974269,0.001489211,0.0007741462,0.0001975817,0.000002150642],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.03486551,0.0004306582,0.9605805,0.0002156747,0.00006767736,0.00001445633,0.0001206123,0.0006647006,0.003040342],"genre_scores_gemma":[0.8651921,0.0004378218,0.1276767,0.000223179,0.00007451773,0.00003483391,0.0003096318,0.00006318902,0.005988062],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.004738674,"threshold_uncertainty_score":0.009422183,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01334799843984631,"score_gpt":0.2011468115754178,"score_spread":0.1877988131355715,"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."}}