{"id":"W3126415943","doi":"10.1109/tvt.2021.3055769","title":"Joint Optimization of BS Clustering and Power Control for NOMA-Enabled CoMP Transmission in Dense Cellular Networks","year":2021,"lang":"en","type":"article","venue":"IEEE Transactions on Vehicular Technology","topic":"Advanced Wireless Communication Technologies","field":"Engineering","cited_by":35,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"Ministry of Education of the People's Republic of China; National Natural Science Foundation of China","keywords":"Cluster analysis; Noma; Computer science; Transmission (telecommunications); Power control; Base station; Interference (communication); Cellular network; Spectral efficiency; Computer network; Single antenna interference cancellation; Power (physics); Mathematical optimization; Decoding methods; Telecommunications link; Algorithm; Telecommunications; Mathematics; Channel (broadcasting)","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":[0.00009159571,0.0001734327,0.0003465888,0.0004196082,0.0000791894,0.000009580833,0.0001569879,0.0004067986,0.00001356627],"category_scores_gemma":[0.00001161365,0.0001960731,0.00007124805,0.0005077586,0.0001158926,0.0000843806,0.00000382213,0.0003957984,5.329023e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00006932731,"about_ca_system_score_gemma":0.00001389244,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000001944672,"about_ca_topic_score_gemma":0.0000159495,"domain_scores_codex":[0.9990591,0.00003096692,0.0003814572,0.0002268354,0.00007053154,0.0002310584],"domain_scores_gemma":[0.999276,0.00008516466,0.0000554988,0.0004628666,0.00009272219,0.00002776284],"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":[0.00002367962,0.00005849532,0.000007329548,0.00005479005,0.00004000859,0.000008108801,0.00003415375,0.9119456,0.07058851,0.0001268161,0.000001881909,0.01711057],"study_design_scores_gemma":[0.001090368,0.0000600675,0.000009441944,0.00008907528,0.00001908234,0.00001511927,0.000163775,0.7323189,0.2657639,0.0002057115,0.0001290171,0.0001354909],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.04056088,0.001730484,0.9562538,0.000384899,0.00008980085,0.0003800395,0.00000882359,0.0005761433,0.00001515054],"genre_scores_gemma":[0.9375705,0.00100496,0.06124501,0.00001714419,0.00000232472,0.0001078572,0.000005338602,0.00003915899,0.000007724406],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.8970096,"threshold_uncertainty_score":0.7995631,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.008568781077452327,"score_gpt":0.2060731603697689,"score_spread":0.1975043792923166,"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."}}