{"id":"W4385882106","doi":"10.1109/bmsb58369.2023.10211504","title":"DRL based Flexible LDM Scheme for Mixed Unicast-Broadcast Transmission in 5G","year":2023,"lang":"en","type":"article","venue":"","topic":"Telecommunications and Broadcasting Technologies","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"Communications Research Centre Canada","funders":"National Natural Science Foundation of China","keywords":"Unicast; Computer science; Scheduling (production processes); Computer network; Scheme (mathematics); Broadcasting (networking); Markov chain; Markov process; Markov decision process; Multimedia Broadcast Multicast Service; Multicast; Distributed computing; Mathematical optimization","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":[],"consensus_categories":[],"category_scores_codex":[0.0001968789,0.0001264158,0.0001495549,0.0003118507,0.00006701893,0.00002597856,0.0003678004,0.0001269177,0.00005293138],"category_scores_gemma":[0.00005979557,0.0001179211,0.00005942008,0.0008541339,0.00002997651,0.00007374564,0.00003542383,0.0001625023,0.00005210441],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004399029,"about_ca_system_score_gemma":0.00001887732,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002250718,"about_ca_topic_score_gemma":0.00001911488,"domain_scores_codex":[0.9992464,0.00001155025,0.0002239684,0.0001490252,0.00007114995,0.0002979765],"domain_scores_gemma":[0.9992752,0.0001744709,0.00001477807,0.0004752441,0.00002530102,0.00003500492],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0000283851,0.0001090096,0.0005298005,0.0002855204,0.00003091629,0.000005027859,0.0001420372,0.0452578,0.08040102,0.003243462,0.05094204,0.819025],"study_design_scores_gemma":[0.0006458343,0.00003766051,0.0006900959,0.00007375592,0.000004315087,9.813435e-7,0.0001815631,0.6806481,0.04933516,0.0005855816,0.2675946,0.0002023788],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6690997,0.002214984,0.2615188,0.01060783,0.0005066365,0.001888398,0.00007201584,0.03021486,0.02387672],"genre_scores_gemma":[0.8476248,0.0002098574,0.15104,0.00002245218,0.00001304303,0.0001042455,0.00005605845,0.00004477395,0.0008848186],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.8188226,"threshold_uncertainty_score":0.4808682,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03536338659745954,"score_gpt":0.2682443992246508,"score_spread":0.2328810126271913,"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."}}