{"id":"W3015657675","doi":"10.1109/twc.2020.2985038","title":"CoMP-NOMA in the SWIPT Networks","year":2020,"lang":"en","type":"article","venue":"IEEE Transactions on Wireless Communications","topic":"Advanced Wireless Communication Technologies","field":"Engineering","cited_by":26,"is_retracted":false,"has_abstract":true,"ca_institutions":"Queen's University","funders":"Science and Engineering Research Council","keywords":"Noma; Computer science; Benchmark (surveying); Transmission (telecommunications); Spectral efficiency; Quality of service; Wireless; Energy consumption; Computer network; Energy (signal processing); Channel (broadcasting); Telecommunications; Electrical engineering; Engineering; Telecommunications link; Mathematics; Statistics","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":[0.00181753,0.001073545,0.0008459727,0.0005264565,0.0008040568,0.001631418,0.0008693972,0.0009373812,0.0009634004],"category_scores_gemma":[0.004433407,0.000498243,0.0007418777,0.001295825,0.00140569,0.001476091,0.001111239,0.0008390057,0.0003135483],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007920378,"about_ca_system_score_gemma":0.0009884015,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00180353,"about_ca_topic_score_gemma":0.002274444,"domain_scores_codex":[0.9982413,0.0008080438,0.00006114177,0.00023141,0.0004275927,0.0002305669],"domain_scores_gemma":[0.9968912,0.002219091,0.0003082715,0.0002041002,0.0003173432,0.00006000357],"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":[0.0001749529,0.0001146581,0.00154769,0.0005436483,0.0002522955,0.001183996,0.0002344466,0.7935553,0.009073053,0.1255301,0.00206723,0.06572261],"study_design_scores_gemma":[0.00002326666,0.0003214002,0.0006684969,0.00005442136,0.00008219144,0.001047728,0.0001156855,0.9482679,0.003631875,0.0405475,0.005209572,0.00002988137],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.06574257,0.007389251,0.8928985,0.000556818,0.0002997892,0.0001035523,0.000156181,0.0001486736,0.03270474],"genre_scores_gemma":[0.9204739,0.003988724,0.06996194,0.0003584936,0.0002628808,0.0001426849,0.00006237093,0.00003664004,0.004712301],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.00181753,"threshold_uncertainty_score":0.009612083,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0362513911193534,"score_gpt":0.2548069949818027,"score_spread":0.2185556038624493,"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."}}