{"id":"W2338408058","doi":"10.1049/iet-spr.2015.0223","title":"Hierarchy precoder design for multi‐cell multiuser multiple‐input–multiple‐output wireless networks with interference alignment","year":2016,"lang":"en","type":"article","venue":"IET Signal Processing","topic":"Advanced MIMO Systems Optimization","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"Concordia University","funders":"Program for New Century Excellent Talents in University; Natural Science Foundation of Jiangsu Province; National Natural Science Foundation of China","keywords":"Computer science; Precoding; Interference (communication); Base station; Zero-forcing precoding; Hierarchy; Transmitter power output; Interference alignment; Data stream mining; Key (lock); Data stream; Algorithm; MIMO; Transmitter; Telecommunications; Beamforming; Data mining; 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":[0.000283772,0.0005180289,0.0004304515,0.0001352071,0.0002165202,0.0001248536,0.0003280675,0.0002175317,0.00001508464],"category_scores_gemma":[0.00004495183,0.0003761222,0.000078823,0.0002226552,0.0001002987,0.0008072748,0.00005236093,0.0002015151,0.00001263615],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002691465,"about_ca_system_score_gemma":0.0000659629,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000006351073,"about_ca_topic_score_gemma":0.00002358396,"domain_scores_codex":[0.9977383,0.00007463431,0.0005749784,0.0006196453,0.0002298258,0.0007626215],"domain_scores_gemma":[0.99852,0.0005110297,0.0002079133,0.0002917748,0.0002718033,0.0001975008],"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":[0.0002643215,0.00007794172,0.001163612,0.0003430753,0.00004733147,0.000004744558,0.0006843494,0.8947704,0.02801371,0.000001885584,0.0001685242,0.07446008],"study_design_scores_gemma":[0.003250201,0.0001410792,0.0000495528,0.001031327,0.00003622618,0.000008320011,0.000111471,0.9435609,0.05099349,0.00001482447,0.0001881882,0.0006144149],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.004659968,0.0006272066,0.9922998,0.00002296783,0.0001915478,0.001467178,0.00002282879,0.0006432706,0.00006522011],"genre_scores_gemma":[0.7721205,0.00003118065,0.2265616,0.00002913282,0.0001680854,0.0004689314,0.00001399708,0.0001722007,0.0004344285],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.7674605,"threshold_uncertainty_score":0.999869,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02972702995818889,"score_gpt":0.2351097466068615,"score_spread":0.2053827166486726,"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."}}