{"id":"W2054175753","doi":"10.1109/glocom.2011.6133674","title":"Performance Analysis of Null-Steering Beamformers in Cognitive Radio Systems","year":2011,"lang":"en","type":"article","venue":"","topic":"Cognitive Radio Networks and Spectrum Sensing","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"","keywords":"Transmitter; Underlay; Cognitive radio; Beamforming; Interference (communication); Computer science; Channel state information; Antenna (radio); Secondary source; Electronic engineering; Channel (broadcasting); Telecommunications; Engineering; Signal-to-noise ratio (imaging); 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":[0.00321202,0.00149956,0.0008196485,0.000534724,0.0003402809,0.001297638,0.0006868875,0.00128582,0.001864276],"category_scores_gemma":[0.01785988,0.0004132003,0.000365321,0.0007234259,0.001060358,0.001213031,0.001189907,0.0005340605,0.0006519548],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007045436,"about_ca_system_score_gemma":0.001125378,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00221841,"about_ca_topic_score_gemma":0.001227603,"domain_scores_codex":[0.9978721,0.0009888429,0.00008551562,0.0001825529,0.0005207639,0.0003502073],"domain_scores_gemma":[0.9872583,0.009319774,0.0008292614,0.0004441782,0.001886271,0.0002622872],"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":[0.0008306484,0.00009259034,0.002981566,0.0001452705,0.0001043741,0.0001172975,0.0001209528,0.9351826,0.01177373,0.01164749,0.0004204849,0.03658302],"study_design_scores_gemma":[0.00003207286,0.0003606265,0.001055738,0.00001734822,0.00004151142,0.000113291,0.00004579715,0.9890526,0.004837349,0.004205032,0.0002154423,0.00002325905],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2255544,0.001121993,0.7622458,0.0003774735,0.00005280067,0.00006224822,0.0001296891,0.0004359104,0.01001968],"genre_scores_gemma":[0.9754401,0.0003640091,0.02257678,0.00006837754,0.0000292945,0.00004315381,0.00009179521,0.0000334113,0.001353109],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.00321202,"threshold_uncertainty_score":0.01698697,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02799221531372774,"score_gpt":0.2292814802504689,"score_spread":0.2012892649367412,"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."}}