{"id":"W3182194795","doi":"10.1109/mass52906.2021.00011","title":"QoS-Aware Load Balancing in Wireless Networks using Clipped Double Q-Learning","year":2021,"lang":"en","type":"preprint","venue":"","topic":"Advanced MIMO Systems Optimization","field":"Engineering","cited_by":13,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Ottawa","funders":"","keywords":"Computer science; Handover; Load balancing (electrical power); Computer network; Quality of service; Throughput; Wireless network; Radio resource management; Packet loss; Context (archaeology); Distributed computing; Wireless; Network packet; 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":[0.0002971159,0.0004389114,0.0006617947,0.0001909295,0.00006534894,0.0001745849,0.0002162574,0.0006183108,0.00008267811],"category_scores_gemma":[0.0000207477,0.0005332739,0.0001109329,0.0004170632,0.00001396764,0.000236029,0.0003963091,0.001474462,0.000005816203],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001105571,"about_ca_system_score_gemma":0.0001276278,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0006713672,"about_ca_topic_score_gemma":0.0008438901,"domain_scores_codex":[0.9979586,0.00006956344,0.0006754426,0.000555074,0.0002154,0.0005259388],"domain_scores_gemma":[0.9990966,0.00005421169,0.0001458565,0.000460548,0.0001538375,0.00008892818],"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":[0.000007319775,0.000007893934,0.002149709,0.000423922,0.0000361071,0.0000367416,0.0003176025,0.9957999,0.0005254355,0.00001421879,0.00001949706,0.0006616337],"study_design_scores_gemma":[0.0005259968,0.000003344756,0.00007305053,0.001372348,0.00001861096,0.00001096916,0.0005837608,0.9963238,0.0004975914,0.000003763057,0.00005151855,0.000535227],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1053021,0.0007629636,0.8874089,0.000004246191,0.002296413,0.0004368054,0.000001402933,0.0007648985,0.003022371],"genre_scores_gemma":[0.9845073,0.0003866804,0.01406783,0.00001140696,0.000470966,0.00005029228,0.0001450798,0.0001709362,0.0001895053],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.8792052,"threshold_uncertainty_score":0.9997119,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01474969148648399,"score_gpt":0.2448848520916449,"score_spread":0.2301351606051609,"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."}}