{"id":"W4312288984","doi":"10.1109/tcomm.2022.3225163","title":"Deep Reinforcement Learning for Resource Allocation in Multi-Band and Hybrid OMA-NOMA Wireless Networks","year":2022,"lang":"en","type":"article","venue":"IEEE Transactions on Communications","topic":"Advanced Wireless Communication Technologies","field":"Engineering","cited_by":48,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université du Québec à Montréal","funders":"","keywords":"Computer science; Reinforcement learning; Resource allocation; Wireless network; Mathematical optimization; Wireless; Greedy algorithm; Heuristic; Power control; Optimization problem; Spectral efficiency; Noma; Distributed computing; Channel (broadcasting); Power (physics); Algorithm; Mathematics; Artificial intelligence; Computer network; Telecommunications link; 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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001447192,0.0007468986,0.001093781,0.0003554271,0.0004114537,0.0008495925,0.001096182,0.0008901122,0.001172749],"category_scores_gemma":[0.002507806,0.0004132856,0.0003796492,0.0004425915,0.0008331144,0.0009364055,0.0009664283,0.00127995,0.0001515454],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001303261,"about_ca_system_score_gemma":0.001191211,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.007228143,"about_ca_topic_score_gemma":0.00823396,"domain_scores_codex":[0.9995578,0.0001758665,0.00001806632,0.00006888116,0.00007862913,0.0001006517],"domain_scores_gemma":[0.9989282,0.0007423934,0.0001207709,0.00004534915,0.0001144567,0.0000488254],"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":[0.00003928386,0.00002768164,0.0002486148,0.00002300999,0.00001913949,0.00002399863,0.00001410784,0.983098,0.0003365097,0.00318832,0.0002670683,0.01271426],"study_design_scores_gemma":[0.000002779047,0.000006234011,0.00001988141,0.000001134436,0.000001743648,0.00000184888,0.000001637269,0.9988546,0.00006004577,0.001009614,0.00003945408,9.685606e-7],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.0480805,0.00076203,0.9472382,0.0004131836,0.00006151276,0.00004286705,0.0000386532,0.000340083,0.003023003],"genre_scores_gemma":[0.9596255,0.0002220425,0.03828463,0.0001181961,0.00002865457,0.00008878097,0.00004098077,0.00002322121,0.001568003],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.007228143,"threshold_uncertainty_score":0.01437211,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.026820198413611,"score_gpt":0.2564614589231429,"score_spread":0.2296412605095319,"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."}}