{"id":"W2999556263","doi":"10.1109/twc.2020.2964551","title":"Low-Complexity User Selection Algorithms for Multiuser Transmissions in mmWave WLANs","year":2020,"lang":"en","type":"article","venue":"IEEE Transactions on Wireless Communications","topic":"Millimeter-Wave Propagation and Modeling","field":"Engineering","cited_by":19,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"","keywords":"Computer science; Telecommunications link; Beamforming; Orthogonality; Selection (genetic algorithm); Channel state information; Algorithm; Transmission (telecommunications); Computational complexity theory; Channel (broadcasting); Selection algorithm; Beamwidth; Interference (communication); Multi-user; Multiuser detection; Computer network; User equipment; Wireless; Base station; Telecommunications; Antenna (radio); Mathematics; Machine learning; Code division multiple access","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":[0.001211804,0.001003755,0.001021257,0.0005700684,0.0006688082,0.0009311957,0.001176313,0.0008390617,0.002205156],"category_scores_gemma":[0.003776317,0.0004335393,0.0004705702,0.0008303651,0.0005665052,0.00109375,0.001081715,0.001167385,0.0008748432],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006160006,"about_ca_system_score_gemma":0.0008971406,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001357754,"about_ca_topic_score_gemma":0.002192969,"domain_scores_codex":[0.9987273,0.0005247263,0.00005915705,0.0001529956,0.0003686064,0.0001671835],"domain_scores_gemma":[0.9975802,0.001724034,0.0001830788,0.0001695854,0.0002700279,0.00007302516],"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":[0.0003594158,0.0001518113,0.001316314,0.0001593428,0.00008593656,0.0001499333,0.0001849665,0.6414537,0.01326883,0.01968262,0.002811925,0.3203752],"study_design_scores_gemma":[0.00002515549,0.00007369069,0.0001489013,0.000006490761,0.000007056965,0.00007049851,0.00002419572,0.9929361,0.001916923,0.004171237,0.0006103609,0.000009340583],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.006078509,0.0002042552,0.9927471,0.00006783997,0.00001928082,0.00003941192,0.00001534847,0.0002063958,0.000621832],"genre_scores_gemma":[0.4474369,0.000550503,0.5486391,0.0002131731,0.0001294519,0.000308659,0.0001309254,0.00007258103,0.002518754],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002205156,"threshold_uncertainty_score":0.007377028,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.09395727201907246,"score_gpt":0.2919339885999869,"score_spread":0.1979767165809144,"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."}}