{"id":"W4399602013","doi":"10.1109/jsac.2024.3413973","title":"Maximizing the Value of Service Provisioning in Multi-User ISAC Systems Through Fairness Guaranteed Collaborative Resource Allocation","year":2024,"lang":"en","type":"article","venue":"IEEE Journal on Selected Areas in Communications","topic":"IoT and Edge/Fog Computing","field":"Computer Science","cited_by":20,"is_retracted":false,"has_abstract":true,"ca_institutions":"Western University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Provisioning; Resource allocation; Computer network; Resource management (computing); Max-min fairness; Service (business); Quality of service; Distributed computing; Business","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":[0.001522604,0.0001889143,0.0002739217,0.0003852763,0.0004974779,0.000514051,0.002537776,0.0001013873,4.568036e-7],"category_scores_gemma":[0.0002379246,0.0001456515,0.00005235059,0.00484467,0.00006845461,0.0006022499,0.000287809,0.001164284,0.00000925419],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002965292,"about_ca_system_score_gemma":0.0005257784,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002989364,"about_ca_topic_score_gemma":0.0001684629,"domain_scores_codex":[0.9969929,0.001236645,0.0008112187,0.0002697354,0.0003776468,0.0003118159],"domain_scores_gemma":[0.9963344,0.0014761,0.0003264807,0.001159775,0.0006598706,0.0000433968],"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":[0.0001871913,0.00201446,0.005835603,0.0005317794,0.0004618554,0.0001088285,0.2465036,0.4681095,0.01467004,0.2352989,0.007430002,0.01884831],"study_design_scores_gemma":[0.0005674211,0.00007122067,0.003208417,0.00271054,0.00001573683,0.0001297353,0.001683468,0.9761901,0.0007495761,0.0007491375,0.01369592,0.0002286858],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2879412,0.01178576,0.6580132,0.02951417,0.00718079,0.002029199,0.000005418755,0.000469805,0.003060471],"genre_scores_gemma":[0.9844222,0.0001808107,0.01472269,0.000349508,0.0002043965,0.00004037998,0.000005255963,0.00002433065,0.00005040964],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.696481,"threshold_uncertainty_score":0.5939497,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04461792930972024,"score_gpt":0.3184622005094288,"score_spread":0.2738442711997085,"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."}}