{"id":"W2295212900","doi":"10.1109/twc.2015.2503747","title":"Multichannel Analysis of Cell Range Expansion and Resource Partitioning in Two-Tier Heterogeneous Cellular Networks","year":2015,"lang":"en","type":"article","venue":"IEEE Transactions on Wireless Communications","topic":"Advanced MIMO Systems Optimization","field":"Engineering","cited_by":42,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"Alberta Innovates - Technology Futures","keywords":"Computer science; Heterogeneous network; Cellular network; Orthogonal frequency-division multiple access; Macro; Resource allocation; Base station; Computer network; Interference (communication); Distributed computing; Orthogonal frequency-division multiplexing; Wireless network; Wireless; Channel (broadcasting); Telecommunications","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":[0.0001902485,0.0001552072,0.0003031158,0.0004743664,0.0001176474,0.00001841474,0.0002334173,0.0000825898,0.000005453186],"category_scores_gemma":[0.000003322401,0.0001840077,0.00007882893,0.0009882738,0.00008156298,0.0001502053,0.000005483467,0.0002480499,0.000002535616],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001062874,"about_ca_system_score_gemma":0.0000117778,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001525698,"about_ca_topic_score_gemma":0.001140874,"domain_scores_codex":[0.9989626,0.0001526062,0.0004234754,0.0001685734,0.0001136384,0.0001790914],"domain_scores_gemma":[0.9986598,0.0001735587,0.00007950438,0.0009040919,0.00008357713,0.00009942852],"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":[0.00001221961,0.0001008712,0.0001212572,0.00001522953,0.00008549792,8.11718e-7,0.001469411,0.9943238,0.002528404,0.00001262516,0.000004016826,0.001325889],"study_design_scores_gemma":[0.0006217106,0.00001906712,0.00003530253,0.00005457042,0.0001939362,0.000001171979,0.0004223814,0.9882121,0.01023469,0.00000762402,0.00003883293,0.0001586102],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1480425,0.0008831947,0.8504314,0.00001979768,0.0000732273,0.0002227286,0.00001985844,0.0001160386,0.0001911998],"genre_scores_gemma":[0.9939522,0.0004485568,0.005353694,0.00001136877,0.000007995205,0.0001173764,0.00004162226,0.0000377572,0.00002944695],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.8459097,"threshold_uncertainty_score":0.7503615,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02390292857996269,"score_gpt":0.2498690717254664,"score_spread":0.2259661431455037,"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."}}