{"id":"W2206263511","doi":"10.1109/jsac.2015.2452586","title":"Resource Allocation for Heterogeneous Applications With Device-to-Device Communication Underlaying Cellular Networks","year":2015,"lang":"en","type":"article","venue":"IEEE Journal on Selected Areas in Communications","topic":"Advanced Wireless Network Optimization","field":"Engineering","cited_by":64,"is_retracted":false,"has_abstract":true,"ca_institutions":"Simon Fraser University","funders":"","keywords":"Computer science; Cellular network; Quality of service; Resource allocation; Cellular traffic; Computer network; Distributed computing; Shared resource; Wireless; Wireless network; Resource management (computing); Heterogeneous network; Resource (disambiguation); 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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0005362245,0.0002822702,0.0002901377,0.0003756468,0.0005115085,0.00014377,0.00119781,0.000159658,0.000002577957],"category_scores_gemma":[0.00008598556,0.0002990916,0.00005029983,0.001730049,0.00006737027,0.0002864793,0.00006645489,0.0007993802,0.00001268317],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007533953,"about_ca_system_score_gemma":0.0001220382,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000007231053,"about_ca_topic_score_gemma":0.0003287088,"domain_scores_codex":[0.9981354,0.0002830482,0.0006527465,0.0002345214,0.0002757028,0.0004185734],"domain_scores_gemma":[0.9964085,0.0006428204,0.0002426576,0.001618012,0.0007938704,0.0002941436],"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":[0.00006369162,0.0001115852,0.0001829859,0.0000108308,0.00005188811,7.572964e-7,0.0003310076,0.9919735,0.0002959754,0.0005527704,0.000762015,0.005663062],"study_design_scores_gemma":[0.001153442,0.0001698759,0.0001433043,0.0003144413,0.00006091441,0.00006730865,0.0002910653,0.9578435,0.0008096634,0.0005413683,0.03812839,0.0004767449],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01212404,0.002011033,0.9816463,0.001091126,0.0000827419,0.001413348,0.000008859765,0.0003704257,0.001252157],"genre_scores_gemma":[0.9203429,0.0007945024,0.07715379,0.0002980238,0.0001524094,0.0008296889,0.000278096,0.0001211514,0.00002947593],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9082189,"threshold_uncertainty_score":0.9999461,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03567989336848552,"score_gpt":0.2742096157795899,"score_spread":0.2385297224111043,"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."}}