{"id":"W3112166140","doi":"10.1109/lnet.2020.3045070","title":"Power Allocation in CoMP-Empowered C-NOMA Networks","year":2020,"lang":"en","type":"article","venue":"IEEE Networking Letters","topic":"Advanced Wireless Communication Technologies","field":"Engineering","cited_by":22,"is_retracted":false,"has_abstract":true,"ca_institutions":"Concordia University","funders":"Fonds de recherche du Québec – Nature et technologies; Concordia University","keywords":"Noma; Computer science; Single antenna interference cancellation; Mathematical optimization; Quality of service; Interference (communication); Power (physics); Heuristic; Computational complexity theory; Optimization problem; Power control; Scheme (mathematics); Transmission (telecommunications); Cellular network; Power optimization; Channel (broadcasting); Computer network; Telecommunications link; Mathematics; Algorithm; Telecommunications","routes":{"ca_aff":true,"ca_fund":true,"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":[0.0005573303,0.000708285,0.0005616824,0.0003226315,0.0007090488,0.0009875557,0.0007798058,0.0007763614,0.0008159042],"category_scores_gemma":[0.001047576,0.0003864279,0.000278009,0.0007994133,0.0008419517,0.0006085354,0.0007486516,0.0005586388,0.000229975],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006951934,"about_ca_system_score_gemma":0.0008001236,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00374082,"about_ca_topic_score_gemma":0.004716415,"domain_scores_codex":[0.9994733,0.00021978,0.00001428901,0.0001002574,0.00009883226,0.00009346557],"domain_scores_gemma":[0.999579,0.0002319299,0.00006039571,0.00003264084,0.00006772784,0.00002836919],"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":[0.0001185917,0.00004725353,0.0004783309,0.00006884691,0.0000346361,0.0002789037,0.00007671958,0.9378204,0.006514157,0.01981806,0.001312444,0.03343165],"study_design_scores_gemma":[0.000007259945,0.00003719243,0.0001200822,0.000003878697,0.000006345137,0.00006669413,0.00002434625,0.9938035,0.0006933731,0.004396211,0.0008344619,0.000006636589],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.05410968,0.001305275,0.9353899,0.0002593653,0.0001106329,0.00004371258,0.00005281933,0.0001475407,0.00858104],"genre_scores_gemma":[0.9508292,0.0004663562,0.04558327,0.0001315177,0.00006092291,0.00006036395,0.00002986665,0.00002073555,0.002817772],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.00374082,"threshold_uncertainty_score":0.007438064,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01605285362697646,"score_gpt":0.2115908036866127,"score_spread":0.1955379500596363,"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."}}