{"id":"W2892311579","doi":"10.1109/twc.2018.2858242","title":"Outage Probability Analysis and Resolution Profile Design for Massive MIMO Uplink With Mixed-ADC","year":2018,"lang":"en","type":"article","venue":"IEEE Transactions on Wireless Communications","topic":"Advanced MIMO Systems Optimization","field":"Engineering","cited_by":28,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"Youth Science Foundation of Jiangxi Province; Natural Sciences and Engineering Research Council of Canada; National Natural Science Foundation of China","keywords":"Telecommunications link; Computer science; MIMO; Coverage probability; Base station; Sorting; Outage probability; Energy consumption; Signal-to-noise ratio (imaging); Interference (communication); Transmitter power output; Algorithm; Mathematical optimization; Electronic engineering; Transmitter; Mathematics; Statistics; Telecommunications; Decoding methods; Channel (broadcasting); Electrical engineering; Fading; Engineering","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002225617,0.0002062019,0.0002709937,0.0002761901,0.0005894069,0.00004609448,0.0003030107,0.000122623,0.00001416193],"category_scores_gemma":[0.000008842374,0.0002039295,0.00008203866,0.0008597532,0.000287077,0.0002671267,0.000003691559,0.0002072362,0.00001064179],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001682967,"about_ca_system_score_gemma":0.00003654158,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00003052664,"about_ca_topic_score_gemma":0.0008532894,"domain_scores_codex":[0.9989325,0.000120956,0.000333822,0.0002813864,0.00009866857,0.0002326874],"domain_scores_gemma":[0.9978788,0.0003279605,0.00008857886,0.001325437,0.0002944772,0.00008478463],"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":[0.00008944366,0.0001607195,0.00004516075,0.00007736866,0.0004863684,1.623854e-7,0.00070919,0.9859139,0.002297346,0.0004352739,0.00006117483,0.009723873],"study_design_scores_gemma":[0.0004724701,0.000173509,0.0001285975,0.00005162957,0.0004205185,0.000002289011,0.0001412197,0.9810893,0.01695522,0.0001585712,0.000148817,0.0002578569],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.005228192,0.00007087774,0.9923145,0.0001792833,0.00009890164,0.001385364,0.0001248272,0.0003997457,0.0001983141],"genre_scores_gemma":[0.7378429,0.00009577823,0.2608659,0.000008297123,0.00001515662,0.0009993083,0.00003957612,0.00003804372,0.00009511392],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.7326146,"threshold_uncertainty_score":0.8316002,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02963730955265967,"score_gpt":0.2584266335198811,"score_spread":0.2287893239672214,"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."}}