{"id":"W2913551535","doi":"10.1109/twc.2019.2892775","title":"Pilot Decontamination in Noncooperative Massive MIMO Cellular Networks Based on Spatial Filtering","year":2019,"lang":"en","type":"article","venue":"IEEE Transactions on Wireless Communications","topic":"Advanced MIMO Systems Optimization","field":"Engineering","cited_by":26,"is_retracted":false,"has_abstract":true,"ca_institutions":"Memorial University of Newfoundland","funders":"Natural Sciences and Engineering Research Council of Canada; Research and Development Corporation of Newfoundland and Labrador","keywords":"MIMO; Computer science; Telecommunications link; Base station; Transmission (telecommunications); Interference (communication); Filter (signal processing); Channel (broadcasting); Multi-user MIMO; Spatial filter; Enhanced Data Rates for GSM Evolution; Precoding; Real-time computing; Telecommunications; Artificial intelligence; Computer vision","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":[0.0005931061,0.0005449337,0.0005855425,0.0002981312,0.0003968957,0.0004483897,0.0005284441,0.0005771767,0.0003622211],"category_scores_gemma":[0.001824595,0.0002488526,0.0004109156,0.0004364967,0.0007858093,0.0006538006,0.0005201503,0.0004444916,0.0001661355],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003422817,"about_ca_system_score_gemma":0.0006640532,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001989461,"about_ca_topic_score_gemma":0.002284832,"domain_scores_codex":[0.9994038,0.000185423,0.00002719054,0.00007970144,0.0002322877,0.00007157007],"domain_scores_gemma":[0.9989954,0.0005890388,0.0001256128,0.0001005458,0.0001550067,0.00003430877],"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":[0.0003404589,0.0001140271,0.0017918,0.0001620108,0.00008467374,0.0004150367,0.0001905014,0.7244907,0.04267308,0.01838493,0.0009807551,0.2103721],"study_design_scores_gemma":[0.000008962827,0.0001135993,0.0003177972,0.00000558518,0.00001271709,0.00009632162,0.00001633013,0.9916506,0.005006013,0.002297663,0.0004649743,0.000009379517],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.0225687,0.0002343723,0.9761969,0.00005818151,0.00003042825,0.0000165628,0.000009566787,0.0001147992,0.0007703651],"genre_scores_gemma":[0.8817924,0.0004217488,0.1158883,0.0001248081,0.00005756205,0.00006278657,0.00003672388,0.00001458161,0.001601167],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.001989461,"threshold_uncertainty_score":0.003955781,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01400974568878744,"score_gpt":0.2317750302203722,"score_spread":0.2177652845315848,"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."}}