{"id":"W2803073251","doi":"10.1109/twc.2018.2825380","title":"Two-Timescale Hybrid RF-Baseband Precoding With MMSE-VP for Multi-User Massive MIMO Broadcast Channels","year":2018,"lang":"en","type":"article","venue":"IEEE Transactions on Wireless Communications","topic":"Advanced MIMO Systems Optimization","field":"Engineering","cited_by":23,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Precoding; Baseband; Codebook; Minimum mean square error; Computer science; MIMO; Algorithm; Zero-forcing precoding; Telecommunications link; Channel state information; Beamforming; Mathematical optimization; Mathematics; Telecommunications; Bandwidth (computing); Wireless; Statistics","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":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0001259236,0.0003220483,0.000310353,0.0002418821,0.0007827487,0.00007986001,0.0005956904,0.00009431269,0.00004568251],"category_scores_gemma":[0.000005999202,0.0003357876,0.0001101984,0.0003816634,0.0002471619,0.0004355447,0.000005554817,0.0003259473,0.00006682988],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002016059,"about_ca_system_score_gemma":0.00004517729,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00004120605,"about_ca_topic_score_gemma":0.0004416704,"domain_scores_codex":[0.9986708,0.00006681209,0.000413878,0.0003214085,0.0001344834,0.0003925808],"domain_scores_gemma":[0.9976568,0.0002598488,0.0001105202,0.001515723,0.0003207768,0.0001363294],"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":[0.0000738825,0.000287444,0.00001199877,0.0000839674,0.0001968056,8.628198e-7,0.0009313822,0.9765835,0.00815679,0.0002073538,0.0002980015,0.01316794],"study_design_scores_gemma":[0.00220605,0.0001862443,0.000008713057,0.000308715,0.0001147646,0.00001998947,0.0003497617,0.8592224,0.1334402,0.00001964213,0.003572208,0.0005512901],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.004489551,0.000153985,0.9919374,0.0001557967,0.0005470043,0.001205203,0.0002719165,0.0006543195,0.0005848028],"genre_scores_gemma":[0.9000425,0.0001802568,0.09719187,0.00003784434,0.00008958024,0.001086034,0.00006153412,0.0001389114,0.001171482],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.8955529,"threshold_uncertainty_score":0.9999094,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03079377184614592,"score_gpt":0.2748690781755596,"score_spread":0.2440753063294136,"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."}}