{"id":"W2902081258","doi":"10.1007/978-3-030-02158-0_6","title":"Nonlinear Hybrid Precoding for Massive MIMO with Universal Frequency Reuse","year":2018,"lang":"en","type":"book-chapter","venue":"Wireless networks","topic":"Advanced MIMO Systems Optimization","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"McGill University","funders":"","keywords":"Precoding; Computer science; Reuse; MIMO; Frequency reuse; Degradation (telecommunications); Interference (communication); Nonlinear system; Computer network; Electronic engineering; Telecommunications; Beamforming; Base station; Engineering; 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":[0.00007696766,0.0005559408,0.0005645307,0.0001406964,0.0001141148,0.00004734547,0.0004039756,0.0004388325,0.0001198543],"category_scores_gemma":[0.00001047575,0.0005721225,0.0001205685,0.00005016376,0.00009838529,0.0002198836,0.00006574667,0.000437625,0.00003138748],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003273529,"about_ca_system_score_gemma":0.00005030699,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000004049864,"about_ca_topic_score_gemma":0.00004962923,"domain_scores_codex":[0.9985,0.000009271928,0.0004017264,0.0004863983,0.000149255,0.0004533771],"domain_scores_gemma":[0.9985346,0.0001036682,0.000243436,0.0007515145,0.000240475,0.0001263187],"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":[0.000084442,0.000008492678,0.000007436551,0.0003130857,0.0003730008,0.0000628054,0.0001574073,0.9805091,0.00002739726,0.004508812,0.010691,0.003257029],"study_design_scores_gemma":[0.0009598766,0.0001973651,3.794917e-7,0.001735864,0.0001848449,0.0000280759,0.00002725846,0.9599187,0.0001266414,0.001152067,0.03459771,0.00107124],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.0001259458,0.0007360211,0.9276833,0.00001511796,0.00113981,0.001139777,0.0001759613,0.0006546166,0.06832948],"genre_scores_gemma":[0.08570182,0.004755408,0.4477139,0.0001535721,0.01986901,0.0005717638,0.005335969,0.004129537,0.431769],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.4799694,"threshold_uncertainty_score":0.999673,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.00945569797429546,"score_gpt":0.1970175977296318,"score_spread":0.1875618997553364,"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."}}