{"id":"W4386050978","doi":"10.36227/techrxiv.23971845.v1","title":"Deep Reinforcement Learning for RSMA-Based Multi-Functional Wireless Networks","year":2023,"lang":"en","type":"preprint","venue":"","topic":"Energy Harvesting in Wireless Networks","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Carleton University","funders":"","keywords":"Reinforcement learning; Computer science; Wireless; Interference (communication); Energy harvesting; Wireless network; Transmitter power output; Distributed computing; Resource (disambiguation); Energy (signal processing); Computer network; Telecommunications; Artificial intelligence; Transmitter","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":[0.0012217,0.0008631377,0.0009482048,0.0002905224,0.0002830493,0.0007333055,0.00106992,0.001106144,0.00177751],"category_scores_gemma":[0.004002515,0.0004440319,0.0003682949,0.0002895667,0.001043318,0.0007359666,0.0009468294,0.001660031,0.0002532085],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001383314,"about_ca_system_score_gemma":0.001228352,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.008861545,"about_ca_topic_score_gemma":0.00748035,"domain_scores_codex":[0.9996228,0.0001437607,0.00001505464,0.00006760328,0.00006959562,0.00008121938],"domain_scores_gemma":[0.9983814,0.001138267,0.0001401792,0.00006172125,0.0001894779,0.00008894067],"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":[0.00002763383,0.00002242487,0.0002856179,0.00002033673,0.00001402666,0.00002254545,0.00001626777,0.9877021,0.0002571791,0.003562529,0.0003457671,0.007723574],"study_design_scores_gemma":[0.000001725215,0.00000460516,0.00001249316,0.000001141315,8.68721e-7,0.000001041208,0.000001083519,0.9989733,0.00003433325,0.0009303744,0.00003842718,6.276827e-7],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.0495645,0.0007952809,0.944293,0.0007629332,0.00008477997,0.00004639254,0.00006329689,0.0005192766,0.003870626],"genre_scores_gemma":[0.9574531,0.0002372161,0.0381193,0.0002202208,0.00004100128,0.0001136288,0.00009201556,0.00005146514,0.003672046],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.008861545,"threshold_uncertainty_score":0.01761991,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03797914867008746,"score_gpt":0.242106091162932,"score_spread":0.2041269424928446,"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."}}