{"id":"W4288617802","doi":"10.48550/arxiv.1901.10535","title":"Generalized Coordinated Multipoint (GCoMP)-Enabled NOMA: Outage,\\n Capacity, and Power Allocation","year":2019,"lang":"","type":"preprint","venue":"arXiv (Cornell University)","topic":"Advanced Wireless Communication Technologies","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Manitoba","funders":"","keywords":"Noma; Computer science; Base station; Transmission (telecommunications); Enhanced Data Rates for GSM Evolution; Computer network; Power (physics); Expression (computer science); Outage probability; Telecommunications link; Cellular network; Throughput; Mathematical optimization; Wireless; Mathematics; Telecommunications; Fading; 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":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0003826716,0.001091448,0.001216078,0.000817764,0.0003822121,0.0001646288,0.00207623,0.001294423,0.0001928981],"category_scores_gemma":[0.000200282,0.001429391,0.000314303,0.001035186,0.0006296833,0.000746728,0.003320606,0.002018348,0.0003109035],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001060434,"about_ca_system_score_gemma":0.00011278,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0003859705,"about_ca_topic_score_gemma":0.0000892707,"domain_scores_codex":[0.9962087,0.0003055349,0.0007687526,0.001705044,0.0001523982,0.0008595718],"domain_scores_gemma":[0.9947608,0.0003220018,0.0006489213,0.003482686,0.0005258726,0.0002597917],"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":[0.0001107924,0.0001584893,0.002833146,0.0003804226,0.0004578672,0.0000463224,0.0004343208,0.9319187,0.004893725,0.05799503,0.0001004437,0.0006707483],"study_design_scores_gemma":[0.003820728,0.0001107234,0.003471355,0.0004007628,0.0002244493,0.00001545688,0.00109264,0.9685077,0.006086833,0.01220697,0.002171448,0.001890903],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6580169,0.0005561124,0.3363966,0.0001754273,0.0005938929,0.001313801,0.00006090584,0.001260029,0.001626299],"genre_scores_gemma":[0.9873144,0.005124318,0.005378007,0.00004208836,0.00002742257,0.00001188009,0.0001210756,0.000159103,0.001821728],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3310186,"threshold_uncertainty_score":0.9988155,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05292886917356927,"score_gpt":0.1841438490882472,"score_spread":0.131214979914678,"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."}}