{"id":"W4282828886","doi":"10.1016/j.asoc.2022.109152","title":"Artificial Bee optimization aided joint user association and resource allocation in HCRAN","year":2022,"lang":"en","type":"article","venue":"Applied Soft Computing","topic":"Advanced MIMO Systems Optimization","field":"Engineering","cited_by":8,"is_retracted":false,"has_abstract":false,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Computer science; Heterogeneous network; Resource allocation; Radio access network; Cloud computing; Distributed computing; Cellular network; Orthogonal frequency-division multiplexing; Efficient energy use; Base station; Baseband; Joint (building); Energy consumption; Computer network; Wireless network; Wireless; Mobile station; Telecommunications; Bandwidth (computing); Engineering","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":[0.0005886127,0.0004735533,0.0006401541,0.0002944371,0.0004001544,0.0006876437,0.0007457227,0.0007984659,0.004243136],"category_scores_gemma":[0.001295931,0.0002549418,0.0003510216,0.000366023,0.0003042342,0.0004552775,0.0005388249,0.0006186491,0.0004860024],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004644941,"about_ca_system_score_gemma":0.0009464289,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.007492986,"about_ca_topic_score_gemma":0.01312566,"domain_scores_codex":[0.999657,0.0001319753,0.00001274623,0.00004559641,0.00008116286,0.00007147196],"domain_scores_gemma":[0.9994797,0.0002871327,0.00002690565,0.00004303187,0.000140543,0.00002257233],"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":[0.0001244683,0.0000554978,0.0003625845,0.00003512561,0.0000328409,0.00005390072,0.00003529256,0.9312837,0.002840524,0.001959522,0.0009852109,0.06223129],"study_design_scores_gemma":[0.000002630672,0.00001140014,0.0000436974,0.000001271113,0.0000022776,0.000003960581,0.000003215449,0.9991816,0.0004550814,0.000145678,0.0001478554,0.000001302779],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.1098351,0.000530736,0.8743368,0.0002806027,0.0001712621,0.0001152519,0.00009591479,0.001266187,0.01336816],"genre_scores_gemma":[0.8359712,0.0001127117,0.1551216,0.0001820188,0.00003856085,0.0001170133,0.00008579357,0.00007431701,0.008296724],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.007492986,"threshold_uncertainty_score":0.01489878,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.008901281676998385,"score_gpt":0.1976590600975877,"score_spread":0.1887577784205893,"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."}}