{"id":"W2910090048","doi":"10.1109/tvt.2019.2893072","title":"Nonlinear Hybrid Precoding for Coordinated Multi-Cell Massive MIMO Systems","year":2019,"lang":"en","type":"article","venue":"IEEE Transactions on Vehicular Technology","topic":"Advanced MIMO Systems Optimization","field":"Engineering","cited_by":18,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Precoding; Baseband; MIMO; Zero-forcing precoding; Computer science; Channel state information; Minimum mean square error; Kalman filter; Control theory (sociology); Spatial correlation; Algorithm; Multi-user MIMO; Channel (broadcasting); Mathematics; Telecommunications; Wireless; Bandwidth (computing); Artificial intelligence; Statistics; Estimator","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":[0.0003470033,0.0003627782,0.0003812859,0.0001170487,0.0001784579,0.0005865686,0.0003426833,0.0003534936,0.0007019647],"category_scores_gemma":[0.0009730922,0.0001543666,0.0002052246,0.000297466,0.0004383037,0.0005421917,0.0005280582,0.0003727553,0.0001755044],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003173163,"about_ca_system_score_gemma":0.0003362473,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00213272,"about_ca_topic_score_gemma":0.00208188,"domain_scores_codex":[0.9997904,0.00007586805,0.000007045369,0.00003757213,0.0000640033,0.00002521115],"domain_scores_gemma":[0.9996735,0.0001637448,0.00004914447,0.0000379384,0.00006352577,0.00001223008],"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":[0.00005674813,0.00001714903,0.0004293945,0.00004999864,0.00003287749,0.00009907221,0.00005930949,0.9530928,0.005942474,0.01359643,0.0004608665,0.02616293],"study_design_scores_gemma":[0.000003362745,0.00003282448,0.00008358203,0.000002374405,0.000003684255,0.00001795435,0.000009708458,0.9970251,0.00071131,0.001851321,0.0002555698,0.00000325619],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.04577386,0.0003790149,0.949639,0.0001403013,0.0000472987,0.00001997972,0.00004431596,0.0001224184,0.003833848],"genre_scores_gemma":[0.9512007,0.0002693926,0.04604382,0.00007232295,0.00003720673,0.00003672692,0.00003864654,0.00001221098,0.002288956],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.00213272,"threshold_uncertainty_score":0.004240572,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.008386314877156426,"score_gpt":0.2155800307481012,"score_spread":0.2071937158709447,"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."}}