{"id":"W2410463341","doi":"10.1109/tvt.2016.2575059","title":"Cooperative Beamforming for Cognitive-Radio-Based Broadcasting Systems in Presence of Asynchronous Interference","year":2016,"lang":"en","type":"article","venue":"IEEE Transactions on Vehicular Technology","topic":"Advanced MIMO Systems Optimization","field":"Engineering","cited_by":9,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia, Okanagan Campus; University of British Columbia","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Beamforming; Asynchronous communication; Computer science; Interference (communication); WSDMA; Convex optimization; Optimization problem; Mathematical optimization; Cognitive radio; Transmission (telecommunications); Channel (broadcasting); Single antenna interference cancellation; Broadcasting (networking); Telecommunications; Algorithm; Computer network; Mathematics; Regular polygon; Wireless; Precoding; MIMO","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001110706,0.0001805126,0.0003016631,0.0005253659,0.00005572019,0.000007365471,0.0001493462,0.0002255521,0.000005115121],"category_scores_gemma":[0.00006172164,0.0001574076,0.00004946407,0.0004502619,0.0001228632,0.0001669487,0.000001166915,0.0001714576,0.000005000448],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000181553,"about_ca_system_score_gemma":0.0000360593,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0000151083,"about_ca_topic_score_gemma":0.00005696081,"domain_scores_codex":[0.9989842,0.00002878874,0.0003943438,0.0002508012,0.00007383384,0.0002680076],"domain_scores_gemma":[0.999211,0.0003098998,0.00007777289,0.0002061924,0.0001664396,0.00002872583],"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":[0.00003297896,0.00004245146,0.00003276539,0.000134664,0.00003290709,0.000003390882,0.00006417497,0.8827157,0.09176176,0.0001194888,0.000001081287,0.02505862],"study_design_scores_gemma":[0.001167126,0.0002518515,0.000004196231,0.00136107,0.00001961966,0.00001707147,0.0002654687,0.5702385,0.4264341,0.00002777441,0.00002005619,0.0001931789],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.06801692,0.0001987429,0.9302584,0.00003205082,0.0002925521,0.0007839572,0.00006370382,0.0003275033,0.00002615616],"genre_scores_gemma":[0.9926728,0.00002804863,0.00667059,0.000002710857,0.00001017916,0.0005564672,0.000002071401,0.00003883144,0.00001827004],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9246559,"threshold_uncertainty_score":0.6418895,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01177645054450975,"score_gpt":0.2330583165407459,"score_spread":0.2212818659962361,"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."}}