{"id":"W3164928475","doi":"10.1109/tgcn.2021.3083205","title":"Rate and Energy Efficiency Improvements of Massive MIMO-Based Stochastic Cellular Networks With NOMA","year":2021,"lang":"en","type":"article","venue":"IEEE Transactions on Green Communications and Networking","topic":"Advanced Wireless Communication Technologies","field":"Engineering","cited_by":17,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université du Québec à Montréal; Concordia University; University of Toronto","funders":"Natural Science Foundation of Beijing Municipality","keywords":"Cellular network; Computer science; Telecommunications link; MIMO; Base station; Noma; Spectral efficiency; Transmitter power output; Interference (communication); Efficient energy use; Stochastic geometry; Precoding; Orthogonal frequency-division multiple access; Electronic engineering; Computer network; Real-time computing; Telecommunications; Beamforming; Mathematics; Engineering; Statistics; Electrical engineering; Orthogonal frequency-division multiplexing; Transmitter","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":[0.001668715,0.0005904084,0.0006031484,0.0003603082,0.0003164297,0.0007946562,0.000535047,0.0004479472,0.0005106822],"category_scores_gemma":[0.00576032,0.0002529209,0.000344682,0.0005324338,0.0006930405,0.0006008149,0.000898797,0.0003806828,0.0001147184],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007843449,"about_ca_system_score_gemma":0.0005687401,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001392398,"about_ca_topic_score_gemma":0.001645261,"domain_scores_codex":[0.9989806,0.0004900605,0.00002792495,0.00008224837,0.0002473342,0.0001718675],"domain_scores_gemma":[0.9964993,0.002429408,0.0003910515,0.0002593,0.0003454919,0.0000754237],"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":[0.00008012198,0.00003364492,0.0009825691,0.0000332788,0.00002371776,0.00008606276,0.00002916171,0.9775301,0.004680202,0.01075043,0.0002071129,0.005563732],"study_design_scores_gemma":[0.000004335572,0.00004077248,0.0004364139,0.000003866197,0.000006923851,0.00003693786,0.00001103746,0.9963794,0.001065209,0.001892162,0.0001172016,0.000005644275],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5466822,0.001054132,0.4414624,0.0004746254,0.00008265722,0.00004433098,0.0001675346,0.0002979151,0.009734169],"genre_scores_gemma":[0.9948955,0.0001559658,0.004546723,0.00002580192,0.00001315388,0.000009853382,0.00001911232,0.000006219127,0.0003276003],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.001668715,"threshold_uncertainty_score":0.008825123,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01267447699220218,"score_gpt":0.2079763426241809,"score_spread":0.1953018656319788,"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."}}