{"id":"W3124703125","doi":"10.1109/tcomm.2021.3053040","title":"Joint Resource Allocation for Linear Precoding in Downlink Massive MIMO Systems","year":2021,"lang":"en","type":"article","venue":"IEEE Transactions on Communications","topic":"Advanced MIMO Systems Optimization","field":"Engineering","cited_by":18,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Precoding; Telecommunications link; Zero-forcing precoding; MIMO; Mathematical optimization; Maximization; Optimization problem; Computer science; Resource allocation; Mathematics; Control theory (sociology); Telecommunications; Beamforming; Computer network","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":[0.0001906204,0.0001622351,0.0002201683,0.0002163914,0.0002400796,0.00004379441,0.0002771798,0.0001325517,0.00001108696],"category_scores_gemma":[0.00002707399,0.0002007666,0.00008765254,0.0004892945,0.00003539235,0.0002117846,0.000003454487,0.0003204268,0.00002543837],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003170132,"about_ca_system_score_gemma":0.00005121059,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002326677,"about_ca_topic_score_gemma":0.0002927431,"domain_scores_codex":[0.9988524,0.0001230476,0.0005085763,0.000209398,0.00009513202,0.0002114618],"domain_scores_gemma":[0.9981318,0.0003340741,0.0000711853,0.001217551,0.0001852296,0.00006021768],"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":[0.000005321637,0.00008643404,0.00000221255,0.00008856474,0.00003435916,5.078561e-7,0.0004284648,0.9910312,0.005418914,0.0004841443,0.0001250205,0.002294831],"study_design_scores_gemma":[0.0005521514,0.00002227544,0.00001135882,0.0002760066,0.00003308792,0.000009099597,0.0008470424,0.972963,0.01655387,0.00006627983,0.008435744,0.0002300385],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.0004220758,0.0007368606,0.9948279,0.0006137491,0.0004283866,0.0007418599,0.00009516453,0.000319827,0.001814239],"genre_scores_gemma":[0.9514943,0.0007370498,0.04579734,0.00002994122,0.00004442866,0.001063051,0.000137977,0.00006598845,0.000629868],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9510723,"threshold_uncertainty_score":0.8187023,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04226174195046146,"score_gpt":0.269726685492047,"score_spread":0.2274649435415856,"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."}}