{"id":"W2055538208","doi":"10.1109/twc.2012.091812.120329","title":"Decentralized Radio Resource Allocation for Single-Network and Multi-Homing Services in Cooperative Heterogeneous Wireless Access Medium","year":2012,"lang":"en","type":"article","venue":"IEEE Transactions on Wireless Communications","topic":"Advanced Wireless Network Optimization","field":"Engineering","cited_by":36,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"Utah Agricultural Experiment Station","keywords":"Computer network; Computer science; Wireless network; Multihoming; Radio resource management; Multi-frequency network; Resource allocation; Heterogeneous network; Heterogeneous wireless network; Wireless; Wireless WAN; Bandwidth allocation; Radio access network; Distributed computing; Wi-Fi array; Bandwidth (computing); Telecommunications; Base station; The Internet","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":[0.0008985278,0.0004234253,0.0006920557,0.0002863742,0.0004620542,0.0005660092,0.0008244531,0.0004858933,0.0007419126],"category_scores_gemma":[0.001692475,0.000249976,0.0002991234,0.0004388861,0.0005841361,0.0008597866,0.0008323031,0.000412706,0.0001297507],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007662518,"about_ca_system_score_gemma":0.0006722587,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001695149,"about_ca_topic_score_gemma":0.002016988,"domain_scores_codex":[0.9994994,0.0002170734,0.00001242909,0.00008044632,0.0001047324,0.0000859449],"domain_scores_gemma":[0.9993969,0.0003409371,0.00009174365,0.00005337226,0.00007129933,0.0000457421],"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":[0.00007068015,0.00007594511,0.0004413385,0.00003962956,0.00002216638,0.00007827221,0.00004978308,0.9615077,0.00463087,0.008016454,0.0003501004,0.02471705],"study_design_scores_gemma":[0.000008043055,0.00002740747,0.00007701532,0.000001137856,0.000003879584,0.00001367518,0.00001055785,0.9979821,0.0003586375,0.001381907,0.0001331499,0.000002379401],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.06686004,0.0002314998,0.930519,0.00009404626,0.00001980739,0.00004843433,0.00001067852,0.00006667995,0.002149764],"genre_scores_gemma":[0.9496847,0.0001177083,0.04910036,0.0000320637,0.00002776378,0.00006659662,0.00001506367,0.00001522874,0.0009404999],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.001695149,"threshold_uncertainty_score":0.005559564,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03230417346238739,"score_gpt":0.2782918667180496,"score_spread":0.2459876932556622,"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."}}