{"id":"W2547895063","doi":"10.1109/ccece.2016.7726729","title":"Optimal power allocation for massive MU-MIMO downlink TDD systems","year":2016,"lang":"en","type":"article","venue":"","topic":"Advanced MIMO Systems Optimization","field":"Engineering","cited_by":9,"is_retracted":false,"has_abstract":true,"ca_institutions":"Concordia University","funders":"","keywords":"Telecommunications link; Precoding; MIMO; Computer science; Coherence time; Base station; Beamforming; Duplex (building); Channel state information; Spectral efficiency; Channel (broadcasting); Electronic engineering; Computer network; Real-time computing; Wireless; Telecommunications; Coherence (philosophical gambling strategy); Engineering; Mathematics; Statistics","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":[0.0004871766,0.0006841626,0.0007299116,0.0003055417,0.000334452,0.0008759191,0.0004586695,0.0005028702,0.001421484],"category_scores_gemma":[0.001391981,0.000377479,0.0002241084,0.0005448411,0.0005623185,0.0005580952,0.0006205211,0.0003695655,0.0002748587],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008158673,"about_ca_system_score_gemma":0.0006767481,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001970113,"about_ca_topic_score_gemma":0.001903055,"domain_scores_codex":[0.9996123,0.0001538101,0.00001291669,0.00005395707,0.00009441159,0.00007270321],"domain_scores_gemma":[0.9996611,0.000186365,0.00004854195,0.00002337114,0.00005669666,0.00002392697],"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":[0.00009449218,0.00002707533,0.0003350061,0.00006115385,0.0000266214,0.00007104762,0.00003115109,0.9725435,0.002887281,0.007104092,0.0005853857,0.01623324],"study_design_scores_gemma":[0.000009821926,0.00003231632,0.0001067824,0.000003477421,0.000006088926,0.00001774074,0.00001653349,0.9961015,0.0004845017,0.002938915,0.0002783298,0.000004008641],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.04785831,0.001016213,0.9417905,0.0003243432,0.00006903637,0.00004831812,0.0001071854,0.0001495711,0.008636558],"genre_scores_gemma":[0.959453,0.0005048307,0.03781604,0.00007712611,0.00004900868,0.00006701842,0.00003583627,0.00002155805,0.001975653],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.001970113,"threshold_uncertainty_score":0.005919576,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.00791470504662198,"score_gpt":0.2189912030545463,"score_spread":0.2110764980079243,"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."}}