{"id":"W4226375155","doi":"10.1109/tsp.2022.3160004","title":"Low-Complexity ADMM-Based Algorithm for Robust Multi-Group Multicast Beamforming in Large-Scale Systems","year":2022,"lang":"en","type":"article","venue":"IEEE Transactions on Signal Processing","topic":"Advanced MIMO Systems Optimization","field":"Engineering","cited_by":18,"is_retracted":false,"has_abstract":true,"ca_institutions":"Ontario Tech University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Mathematical optimization; Computer science; Computational complexity theory; Beamforming; Robustness (evolution); Algorithm; Convex optimization; MIMO; Convergence (economics); Optimization problem; Multicast; Mathematics; Regular polygon; Distributed computing","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":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.000361215,0.0002841915,0.000333681,0.00036817,0.0006566872,0.00007638113,0.0001786508,0.00009287567,0.00003730857],"category_scores_gemma":[0.000002077558,0.0003408575,0.0001061367,0.0005815186,0.00003602784,0.0004194684,0.000002164597,0.0004650955,0.000003928473],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000576908,"about_ca_system_score_gemma":0.00005548178,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001910595,"about_ca_topic_score_gemma":0.00008276461,"domain_scores_codex":[0.9982294,0.00006185526,0.0005447346,0.0003885772,0.0002636064,0.0005118577],"domain_scores_gemma":[0.9994506,0.00009824324,0.00009920033,0.0001737046,0.00008096102,0.00009735139],"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":[0.00003709066,0.0002943712,0.000003330143,0.0004313333,0.00001049503,0.000003979705,0.0003923966,0.933823,0.00220897,0.00000116061,0.00000336391,0.0627905],"study_design_scores_gemma":[0.002054831,0.00008905218,0.000002810247,0.0002171943,0.0000239616,0.0000117926,0.001464152,0.9906749,0.004978253,0.000003702132,0.0001198025,0.0003595893],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.0008481658,0.000239052,0.9965451,0.000007911742,0.0005294286,0.001022459,0.0002993635,0.0004837991,0.00002474891],"genre_scores_gemma":[0.7166402,0.000002141052,0.2821626,0.00002179611,0.0000504163,0.0009398782,0.00003804429,0.00009861231,0.00004626498],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.7157921,"threshold_uncertainty_score":0.9999043,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02942835840473498,"score_gpt":0.2536017955927676,"score_spread":0.2241734371880326,"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."}}