{"id":"W2985957562","doi":"10.1109/tvt.2019.2953281","title":"Capturing the Sparsity and Tracking the Channels for Massive MIMO Networks","year":2019,"lang":"en","type":"article","venue":"IEEE Transactions on Vehicular Technology","topic":"Advanced MIMO Systems Optimization","field":"Engineering","cited_by":7,"is_retracted":false,"has_abstract":true,"ca_institutions":"Western University","funders":"National Natural Science Foundation of China","keywords":"MIMO; Computer science; Channel (broadcasting); Autoregressive model; Overhead (engineering); Spatial correlation; Algorithm; Telecommunications link; Kalman filter; Artificial intelligence; 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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001102965,0.0001478898,0.0001547952,0.0001098662,0.0002293398,0.00002606973,0.0001755197,0.0002209399,0.00000544228],"category_scores_gemma":[0.000004206625,0.0001038357,0.00005933953,0.000240751,0.00007608603,0.0000891636,0.000001853727,0.000370074,0.000007960132],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00005163779,"about_ca_system_score_gemma":0.000004631955,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000005178539,"about_ca_topic_score_gemma":0.00002356207,"domain_scores_codex":[0.9993393,0.00001784412,0.0001507985,0.0001917371,0.0000569523,0.0002433057],"domain_scores_gemma":[0.9994592,0.0001034648,0.00003803904,0.0003375866,0.00004227051,0.00001943754],"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":[0.000005634677,0.000006402032,0.00001251773,0.00002027013,0.00005031452,9.690854e-7,0.0001041561,0.9909303,0.001952227,0.0002912805,0.00001134219,0.00661455],"study_design_scores_gemma":[0.0004004017,0.00005168618,0.00001577707,0.00005066222,0.00005088371,0.00003539694,0.0004717722,0.9577121,0.03959251,0.0004299496,0.001011751,0.0001770736],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.07007036,0.0004079633,0.9268575,0.0006458234,0.0007628858,0.0008273576,0.000005862656,0.0003871848,0.00003503049],"genre_scores_gemma":[0.9986189,0.00009438529,0.0009179413,0.00003860774,0.00004041824,0.0001815978,0.000001255839,0.00003695559,0.00006996535],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9285485,"threshold_uncertainty_score":0.4234297,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.007964038588962056,"score_gpt":0.1992414630396092,"score_spread":0.1912774244506471,"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."}}