{"id":"W2113404306","doi":"10.1109/icc.2010.5501794","title":"Single and Multiple Carrier Designs for Cognitive Radio Systems","year":2010,"lang":"en","type":"article","venue":"","topic":"Cognitive Radio Networks and Spectrum Sensing","field":"Computer Science","cited_by":7,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"","keywords":"Cognitive radio; Robustness (evolution); Computer science; Convex optimization; MIMO; Transmitter; Precoding; Optimization problem; Mathematical optimization; Channel state information; Channel (broadcasting); Linear matrix inequality; Wireless; Algorithm; Regular polygon; Mathematics; Telecommunications","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":[0.002510531,0.001301409,0.0008197645,0.0005948155,0.0004346989,0.001385661,0.0007535201,0.001087463,0.002896939],"category_scores_gemma":[0.005531843,0.0005100809,0.0006139249,0.0007125397,0.0009810864,0.001174697,0.0008449068,0.0009940809,0.0006366021],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008006263,"about_ca_system_score_gemma":0.001045207,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0005481226,"about_ca_topic_score_gemma":0.0006180009,"domain_scores_codex":[0.9983467,0.0007807598,0.000051229,0.0002106457,0.0004966793,0.0001141079],"domain_scores_gemma":[0.9983346,0.001010354,0.000223232,0.0001169188,0.0002690813,0.00004578814],"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":[0.00008645005,0.00007726095,0.0003070848,0.0003487933,0.00008126492,0.0001232565,0.000159927,0.6831586,0.004352295,0.2093014,0.001980333,0.1000234],"study_design_scores_gemma":[0.00003613383,0.0001260263,0.00007039549,0.00003516169,0.00002234159,0.00007032959,0.00002720765,0.9322634,0.001120508,0.06270868,0.003503338,0.00001651247],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.00431704,0.0007354035,0.9913417,0.0001347238,0.00005861473,0.000043581,0.00001538818,0.00003681786,0.003316894],"genre_scores_gemma":[0.5486802,0.002306397,0.4431385,0.0002325462,0.0002794123,0.0004159631,0.00005052663,0.00004916934,0.004847382],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002896939,"threshold_uncertainty_score":0.01327711,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02915084415422509,"score_gpt":0.243414704141642,"score_spread":0.214263859987417,"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."}}