{"id":"W4254290501","doi":"10.32920/ryerson.14644947.v1","title":"Nonconvex and game theory optimization for resource allocation in wireless communications","year":2021,"lang":"en","type":"preprint","venue":"","topic":"Advanced Wireless Network Optimization","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Toronto Metropolitan University; University of Manitoba; University of Toronto","funders":"","keywords":"Mathematical optimization; Computer science; Stackelberg competition; Resource allocation; Optimization problem; Computational complexity theory; Wireless; Game theory; Throughput; Algorithm; Mathematics; Computer network; 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":[0.0002858068,0.0002432751,0.0003066561,0.0001608701,0.00005108308,0.0000929709,0.00029917,0.0003371162,0.00001896957],"category_scores_gemma":[0.00004909259,0.0002978897,0.00004679861,0.0002050084,0.00006620389,0.0001620932,0.0003407555,0.0003855177,5.868874e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001676446,"about_ca_system_score_gemma":0.00004198562,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000007611498,"about_ca_topic_score_gemma":0.00009622952,"domain_scores_codex":[0.9988587,0.0001054014,0.0004126485,0.0003295526,0.00009290686,0.000200755],"domain_scores_gemma":[0.9985579,0.0003283076,0.00009323686,0.0008526242,0.0001172429,0.00005068806],"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":[0.000009445254,0.00002072064,0.00004636659,0.0002139023,0.00002622305,2.596876e-7,0.0004584468,0.9813575,0.00003791845,0.003981201,0.00005590196,0.0137921],"study_design_scores_gemma":[0.0003311335,0.000006057349,0.00005949757,0.0002492991,0.00002458749,0.000001402233,0.0003322211,0.9972544,0.0002388657,0.0009050565,0.0003033083,0.0002941163],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.003971849,0.00159459,0.9908767,0.0001823818,0.0001453368,0.000843988,0.00001389459,0.0003237589,0.002047524],"genre_scores_gemma":[0.730359,0.004263198,0.2619589,0.00007462255,0.00007026149,0.0005749148,0.002471983,0.0001074149,0.0001197181],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.7289178,"threshold_uncertainty_score":0.9999473,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01453718946275156,"score_gpt":0.2464669850191707,"score_spread":0.2319297955564192,"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."}}