{"id":"W4300361987","doi":"10.1109/icc45855.2022.9839273","title":"Decentralized User Scheduling and Beamforming in Multi-cell MIMO Networks","year":2022,"lang":"en","type":"article","venue":"ICC 2022 - IEEE International Conference on Communications","topic":"Advanced MIMO Systems Optimization","field":"Engineering","cited_by":10,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Beamforming; Computer science; Scheduling (production processes); MIMO; Signal-to-interference-plus-noise ratio; Scalability; Leakage (economics); Algorithm; Electronic engineering; Mathematical optimization; Telecommunications; Mathematics; Engineering","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":[0.001145914,0.0006222877,0.001083449,0.000248147,0.0005279648,0.0008657533,0.0007235749,0.0008501318,0.001000474],"category_scores_gemma":[0.002683555,0.000362408,0.0003571066,0.0008468287,0.0008428174,0.001142777,0.0007541484,0.0007361812,0.0002332312],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001005062,"about_ca_system_score_gemma":0.001070075,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002147259,"about_ca_topic_score_gemma":0.002640144,"domain_scores_codex":[0.9990048,0.0004787728,0.00002223652,0.0001491573,0.000216126,0.0001288817],"domain_scores_gemma":[0.9985738,0.000879608,0.0002052771,0.0001100465,0.0001558549,0.00007555628],"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":[0.00003672084,0.00001987945,0.0001652109,0.0000248202,0.00001202244,0.00003583782,0.00001921444,0.9830982,0.001652584,0.007515523,0.0002518815,0.007168128],"study_design_scores_gemma":[0.000005296835,0.00001759206,0.00006091683,0.000001447418,0.000002186435,0.000008246509,0.000008627263,0.9956914,0.0003976921,0.003653705,0.0001501298,0.000002769377],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02658968,0.0002772824,0.9701334,0.0001886121,0.0000425318,0.00003148456,0.00003676799,0.0001054905,0.002594716],"genre_scores_gemma":[0.910388,0.0003860823,0.08652469,0.0001149886,0.00009142404,0.0001049066,0.00004924319,0.00003570642,0.002304971],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002147259,"threshold_uncertainty_score":0.00729233,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06688023611352803,"score_gpt":0.3158072427891416,"score_spread":0.2489270066756135,"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."}}