{"id":"W1976537743","doi":"10.1109/twc.2014.2327030","title":"Partially-Distributed Resource Allocation in Small-Cell Networks","year":2014,"lang":"en","type":"article","venue":"IEEE Transactions on Wireless Communications","topic":"Advanced MIMO Systems Optimization","field":"Engineering","cited_by":34,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Computer science; Resource allocation; Scheduling (production processes); Telecommunications link; Distributed computing; Resource management (computing); Orthogonal frequency-division multiple access; Mathematical optimization; Cellular network; Radio resource management; Computational complexity theory; Orthogonal frequency-division multiplexing; Wireless network; Computer network; Wireless; Algorithm; Mathematics; 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":[0.0006854766,0.0004281658,0.0006639672,0.0002537537,0.0005535848,0.0005646562,0.001091973,0.0004227089,0.0008922988],"category_scores_gemma":[0.001344541,0.0002187491,0.0002791244,0.0004259577,0.0007773978,0.0007503461,0.0007561647,0.0003435321,0.0002079816],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006472622,"about_ca_system_score_gemma":0.0007089062,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002559651,"about_ca_topic_score_gemma":0.003447793,"domain_scores_codex":[0.9994954,0.000196965,0.00001608392,0.00006943746,0.0001307187,0.00009140535],"domain_scores_gemma":[0.9994329,0.0002540572,0.00007390221,0.00009921914,0.00008884222,0.00005106618],"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":[0.00007481556,0.00003531415,0.0003167037,0.00003363103,0.00001731203,0.00005850238,0.00005088673,0.9539335,0.004613519,0.01675441,0.0006515744,0.02345984],"study_design_scores_gemma":[0.000007044694,0.00001641207,0.00004454686,7.838134e-7,0.000002086558,0.000007181919,0.000005173026,0.9959273,0.000463065,0.003316483,0.0002071164,0.000002753031],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.03518416,0.0001843314,0.9625131,0.0001213281,0.00002483213,0.00004715075,0.00002447808,0.0001855374,0.001715107],"genre_scores_gemma":[0.9026172,0.0001333314,0.09572908,0.00007493949,0.0000301628,0.00009366167,0.00003420359,0.00001855507,0.001269031],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002559651,"threshold_uncertainty_score":0.005089521,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0165032129247009,"score_gpt":0.2260339828859279,"score_spread":0.209530769961227,"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."}}