{"id":"W7117630959","doi":"10.23919/jcc.fa.2023-0421.202512","title":"Joint robust beamforming design for WPT-assisted D2D communications in MISO-NOMA: Fractional programming and deep reinforcement learning","year":2025,"lang":"","type":"article","venue":"China Communications","topic":"Advanced Wireless Communication Technologies","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Western University","funders":"","keywords":"Beamforming; Telecommunications link; Reinforcement learning; Efficient energy use; Wireless; Key (lock); Fractional programming; Quadratic programming; Joint (building); Channel state information","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":[0.0008333951,0.0008754602,0.0008700374,0.0002601929,0.0002765787,0.0008472222,0.000680711,0.0009339854,0.0013447],"category_scores_gemma":[0.001795125,0.000379267,0.000455816,0.0003729631,0.0007237869,0.0007248949,0.0009068867,0.00116395,0.000230939],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005552341,"about_ca_system_score_gemma":0.0009601274,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002970835,"about_ca_topic_score_gemma":0.002029085,"domain_scores_codex":[0.9997266,0.00008983129,0.00001189146,0.00006296085,0.00005999288,0.00004875424],"domain_scores_gemma":[0.9994094,0.0003695617,0.00007278738,0.00002036837,0.0000982737,0.00002954397],"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":[0.00003778486,0.00002260092,0.0002582437,0.00004866952,0.00002068947,0.00004832168,0.00003801546,0.9646828,0.001324043,0.008718815,0.0005460001,0.02425403],"study_design_scores_gemma":[0.000002444818,0.00001149614,0.00001455278,0.00000208408,0.000001917646,0.000003652985,0.000003164281,0.998526,0.0001097462,0.001207621,0.0001157762,0.000001565132],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.006443235,0.0002023423,0.9915321,0.0001591852,0.00002621797,0.00001416255,0.00001389559,0.00007263631,0.001536156],"genre_scores_gemma":[0.8357973,0.0005145983,0.159253,0.0002531441,0.00005911764,0.0001831925,0.00008270048,0.00005148038,0.003805469],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002970835,"threshold_uncertainty_score":0.005907118,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06636613994197778,"score_gpt":0.3063590305924715,"score_spread":0.2399928906504938,"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."}}