{"id":"W2135010525","doi":"10.1109/tw.2014.011614.131163","title":"Clustering and Resource Allocation for Dense Femtocells in a Two-Tier Cellular OFDMA Network","year":2014,"lang":"en","type":"article","venue":"IEEE Transactions on Wireless Communications","topic":"Advanced MIMO Systems Optimization","field":"Engineering","cited_by":184,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Manitoba","funders":"","keywords":"Femtocell; Computer science; Cellular network; Computer network; Orthogonal frequency-division multiple access; Cluster analysis; Resource allocation; Femto-; Distributed computing; Frequency-division multiple access; Wireless network; Orthogonal frequency-division multiplexing; Wireless; Channel (broadcasting); Base station; 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":[0.0005168919,0.0004974145,0.0006795527,0.0003781028,0.0005683849,0.0007496746,0.0007148018,0.0006562965,0.0008488193],"category_scores_gemma":[0.0008687513,0.0003292657,0.0003744067,0.0006442149,0.0005238196,0.0006273029,0.0007724532,0.0003293677,0.0001188009],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001076378,"about_ca_system_score_gemma":0.0008740033,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00798174,"about_ca_topic_score_gemma":0.008755187,"domain_scores_codex":[0.9995083,0.000155305,0.00001868356,0.0001265305,0.00008174397,0.0001094629],"domain_scores_gemma":[0.9996885,0.0001492785,0.00005305974,0.00002257399,0.00004881002,0.00003774049],"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":[0.00006079495,0.00005700062,0.0005395731,0.00003172556,0.00001641169,0.00007440495,0.00004680456,0.9721606,0.00362443,0.004732531,0.0006268643,0.0180288],"study_design_scores_gemma":[0.000003260939,0.0000155578,0.0001476915,0.000001292555,0.000002529711,0.0000107431,0.00001499138,0.9984201,0.0002785292,0.0009893582,0.0001129342,0.000002964012],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1267884,0.0003408104,0.8677821,0.000265146,0.00003187706,0.0001081062,0.00008485079,0.0001801026,0.004418639],"genre_scores_gemma":[0.9159327,0.0001647361,0.08152533,0.00006675094,0.000018943,0.00009416669,0.00006158066,0.00001787571,0.002118033],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.00798174,"threshold_uncertainty_score":0.01587057,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01673242376324071,"score_gpt":0.2450569857486859,"score_spread":0.2283245619854452,"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."}}