{"id":"W3132248536","doi":"10.1109/vtc2020-fall49728.2020.9348648","title":"Rate Enhancement for Distributed Massive MIMO Systems with Underlay Spectrum Sharing","year":2020,"lang":"en","type":"article","venue":"","topic":"Advanced MIMO Systems Optimization","field":"Engineering","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"","keywords":"Underlay; Telecommunications link; MIMO; Rayleigh fading; Computer science; Computer network; Channel state information; Interference (communication); Fading; Channel (broadcasting); Selection (genetic algorithm); Multi-user MIMO; Signal-to-noise ratio (imaging); Telecommunications; Wireless","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":[0.001297337,0.001387833,0.0009420898,0.0003469108,0.0003528658,0.001040684,0.0008391776,0.0005541309,0.001311896],"category_scores_gemma":[0.003642767,0.0003026965,0.0005374756,0.0005027187,0.0007720823,0.001104812,0.0012589,0.0008283704,0.0003495877],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006731229,"about_ca_system_score_gemma":0.0004479213,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0007673845,"about_ca_topic_score_gemma":0.00090049,"domain_scores_codex":[0.9989599,0.0003965737,0.00002639536,0.0001266302,0.0003073188,0.0001830627],"domain_scores_gemma":[0.9975439,0.001603658,0.0002440559,0.0002212002,0.0003237298,0.00006359279],"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":[0.0001977444,0.00009757892,0.0009630932,0.0002773838,0.00009127035,0.0004579837,0.000169079,0.9086219,0.03434067,0.03087628,0.0006593085,0.02324768],"study_design_scores_gemma":[0.000007539385,0.0001125104,0.000288589,0.00001206894,0.0000208585,0.0001583189,0.00003639738,0.9895939,0.004434503,0.004959905,0.0003645478,0.00001095077],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1377619,0.002355547,0.8466085,0.0002804741,0.00008443876,0.0000492125,0.0001096587,0.0002285616,0.01252172],"genre_scores_gemma":[0.9828886,0.0005543375,0.01528614,0.00003694337,0.00004307181,0.00002682142,0.00002779273,0.0000197904,0.001116542],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.001387833,"threshold_uncertainty_score":0.006861091,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01529003862573782,"score_gpt":0.2142617660591814,"score_spread":0.1989717274334436,"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."}}