{"id":"W2913038240","doi":"10.1109/tvt.2019.2956921","title":"Resource Allocation in Green Dense Cellular Networks: Complexity and Algorithms","year":2019,"lang":"en","type":"preprint","venue":"IEEE Transactions on Vehicular Technology","topic":"Advanced MIMO Systems Optimization","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université du Québec à Montréal","funders":"","keywords":"Computer science; Upload; Scheduling (production processes); Heuristic; Time complexity; Mathematical optimization; Resource allocation; Computational complexity theory; Algorithm; Channel (broadcasting); Cellular network; Distributed computing; Computer network; Mathematics","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":[0.002360001,0.001065146,0.001585668,0.0008976754,0.0008132748,0.002810975,0.001337572,0.001420936,0.001846442],"category_scores_gemma":[0.008933208,0.0007161787,0.0006525982,0.002094937,0.001466106,0.002553368,0.001797429,0.001666019,0.0002686915],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002988423,"about_ca_system_score_gemma":0.001835206,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.007032277,"about_ca_topic_score_gemma":0.005903897,"domain_scores_codex":[0.9985312,0.0006446777,0.00005003673,0.0001956133,0.0003612433,0.0002171905],"domain_scores_gemma":[0.992012,0.006856629,0.0004420808,0.0002746099,0.0002913339,0.0001233656],"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":[0.00007549068,0.00005810769,0.0005134989,0.0001097206,0.00003159287,0.00004820551,0.00005472118,0.9295136,0.0004356523,0.03834329,0.001646835,0.02916944],"study_design_scores_gemma":[0.00001257886,0.000009329352,0.00008570983,0.000007427439,0.000004998831,0.00001816603,0.00002088579,0.966456,0.0001142138,0.03278574,0.0004806772,0.000004329987],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.0336421,0.00278666,0.9531832,0.001671985,0.0000949831,0.0001396988,0.0001995452,0.0002428983,0.00803894],"genre_scores_gemma":[0.737388,0.003989286,0.2505113,0.0004060955,0.0003724573,0.000495306,0.0003857157,0.0001362428,0.006315622],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.007032277,"threshold_uncertainty_score":0.02168268,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01401459595108723,"score_gpt":0.2205060423876169,"score_spread":0.2064914464365297,"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."}}