{"id":"W1994561857","doi":"10.1109/medhocnet.2010.5546865","title":"On the capacity and scheduling of a multi-sector cell with co-channel interference knowledge","year":2010,"lang":"en","type":"article","venue":"","topic":"Advanced MIMO Systems Optimization","field":"Engineering","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"Communications Research Centre Canada; University of Waterloo","funders":"","keywords":"Computer science; Scheduling (production processes); Computer network; Channel capacity; Co-channel interference; Channel allocation schemes; Interference (communication); Space-division multiple access; Wireless; Channel (broadcasting); White spaces; Schedule; Handover; Telecommunications link; Cognitive radio; Electronic engineering; Telecommunications; Mathematical optimization; Engineering; Mathematics","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00006509478,0.00008460289,0.00008745305,0.00003160684,0.00002593049,0.000009084624,0.0000618605,0.00004035755,0.00002618338],"category_scores_gemma":[0.00002680629,0.00005222914,0.000009212518,0.00005808142,0.00004116735,0.00005737553,0.000009788039,0.0001550312,0.000008151975],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000008768633,"about_ca_system_score_gemma":0.000005014523,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001297849,"about_ca_topic_score_gemma":0.0002105062,"domain_scores_codex":[0.9996964,0.000008683816,0.00009392123,0.00008668534,0.00003041894,0.00008392208],"domain_scores_gemma":[0.9996778,0.00008673069,0.00002376367,0.0001406422,0.00004318227,0.00002789207],"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":[0.00003438171,0.0001123536,0.0008232103,0.0004363066,0.00004199018,8.463556e-7,0.005627966,0.5096747,0.4772081,0.00551461,0.00007895993,0.000446563],"study_design_scores_gemma":[0.0003298271,0.00004802638,0.0001166662,0.00006502534,0.000004173082,0.000002742577,0.0002389527,0.7981809,0.2008426,0.00004413922,0.00001439557,0.0001124948],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.4829099,0.00002267281,0.5127869,0.00000348034,0.00007806081,0.0001176055,0.000002785181,0.00006143416,0.004017116],"genre_scores_gemma":[0.9838687,0.00000383167,0.01596838,0.000004084997,0.00001511286,0.00001069958,6.464281e-7,0.00001603853,0.0001125767],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.5009587,"threshold_uncertainty_score":0.2129843,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02270829837389312,"score_gpt":0.2274822425875124,"score_spread":0.2047739442136192,"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."}}