{"id":"W2154252801","doi":"10.1109/isit.2009.5205839","title":"Throughput enhancements in point-to-multipoint cognitive systems","year":2009,"lang":"en","type":"article","venue":"","topic":"Advanced MIMO Systems Optimization","field":"Engineering","cited_by":14,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"","keywords":"Computer science; Cognitive radio; Interference (communication); Throughput; Channel (broadcasting); Lemma (botany); Base station; Computer network; Channel state information; Cognitive network; Topology (electrical circuits); Algorithm; Telecommunications; Mathematics; 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":[0.001910237,0.0007511785,0.000666184,0.0005662749,0.0003351165,0.001130763,0.0006071028,0.0005306539,0.001026341],"category_scores_gemma":[0.006841085,0.0002367684,0.0003438408,0.0005876216,0.001113299,0.0009809022,0.00111021,0.0004482654,0.0001457156],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008065872,"about_ca_system_score_gemma":0.0004978748,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001118482,"about_ca_topic_score_gemma":0.0008042387,"domain_scores_codex":[0.999087,0.0003127252,0.00002174436,0.00008075371,0.0002335702,0.0002641739],"domain_scores_gemma":[0.9958283,0.003106365,0.0003321223,0.0002587047,0.0003686158,0.0001057946],"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":[0.0003507284,0.0000591047,0.001477838,0.0001085402,0.00005988074,0.0003712926,0.0001584299,0.9175031,0.01848828,0.04399141,0.0004902738,0.01694106],"study_design_scores_gemma":[0.00001381035,0.000147738,0.0007819487,0.000008679698,0.00003152127,0.0001205403,0.00005461256,0.9750481,0.00404461,0.01948232,0.0002530228,0.00001308271],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6386377,0.001505884,0.3417293,0.0005083909,0.00009624287,0.00003503933,0.0001109467,0.0003466214,0.01702987],"genre_scores_gemma":[0.9959766,0.0002046109,0.003334254,0.00002103687,0.00002354703,0.00000793026,0.000007958869,0.00000678487,0.0004172814],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.001910237,"threshold_uncertainty_score":0.01010245,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.009208677488210143,"score_gpt":0.2458695736974744,"score_spread":0.2366608962092642,"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."}}