{"id":"W2140930339","doi":"10.1109/itng.2009.91","title":"Optimized Hybrid Resource Allocation in Wireless Cellular Networks with and without Channel Reassignment","year":2009,"lang":"en","type":"article","venue":"","topic":"Wireless Communication Networks Research","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Windsor","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Channel (broadcasting); Benchmark (surveying); Base station; Channel allocation schemes; Resource allocation; Computer network; Blocking (statistics); Wireless; Mathematical optimization; Mobile telephony; Cellular network; Mobile radio; Telecommunications; Mathematics","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":[0.0007385496,0.0001702243,0.000224464,0.0001525547,0.0001267103,0.0002188188,0.0009948742,0.00005835381,0.000004793892],"category_scores_gemma":[0.000009232468,0.0001399896,0.00001986249,0.0005154347,0.00007501625,0.0003608296,0.0002750222,0.0003061728,0.000003445773],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00008240811,"about_ca_system_score_gemma":0.0000497636,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00004358476,"about_ca_topic_score_gemma":0.00001495332,"domain_scores_codex":[0.9981913,0.0002793406,0.0002659087,0.000456595,0.0004005032,0.0004063313],"domain_scores_gemma":[0.9985188,0.00009595547,0.00008758945,0.001065084,0.00007567562,0.0001568451],"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":[0.0004347155,0.0006432809,0.001791837,0.00001963516,0.00004903047,0.00005425818,0.001098342,0.7307336,0.0009825975,0.05111507,0.002046234,0.2110314],"study_design_scores_gemma":[0.001079779,0.0001272561,0.002105891,0.00006445179,0.000002182557,0.00001475815,0.00003481879,0.994935,0.0009632457,0.0002515814,0.0002242717,0.0001967909],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.03138686,0.0002839553,0.9599406,0.004735131,0.00001576169,0.0004210347,1.363467e-7,0.0001458683,0.003070623],"genre_scores_gemma":[0.9646304,0.0002073772,0.03427486,0.0004098465,0.00003122024,0.00004210299,0.000008577639,0.00001147561,0.0003841785],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9332435,"threshold_uncertainty_score":0.570861,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01613355729583786,"score_gpt":0.2454182782217637,"score_spread":0.2292847209259258,"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."}}