{"id":"W4221135250","doi":"10.1109/twc.2022.3159779","title":"An Online Zero-Forcing Precoder for Weighted Sum-Rate Maximization in Green CoMP Systems","year":2022,"lang":"en","type":"article","venue":"IEEE Transactions on Wireless Communications","topic":"Advanced MIMO Systems Optimization","field":"Engineering","cited_by":15,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia; Carleton University","funders":"National Key Research and Development Program of China; State Key Laboratory of Advanced Metallurgy; Fundamental Research Funds for the Central Universities; Higher Education Discipline Innovation Project; Natural Sciences and Engineering Research Council of Canada; University of Science and Technology Beijing; Research Grants Council, University Grants Committee; Science, Technology and Innovation Commission of Shenzhen Municipality; Natural Science Foundation of Guangdong Province; National Natural Science Foundation of China","keywords":"Maximization; Energy (signal processing); Mathematics; Combinatorics; Computer science; Algorithm; Discrete mathematics; Applied mathematics; Mathematical optimization; Statistics","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":[0.0002922692,0.00022966,0.000295815,0.0004447426,0.0007115674,0.00004929416,0.0007174785,0.00009207289,0.00002167721],"category_scores_gemma":[0.000002463303,0.0002927023,0.00007370694,0.0007565999,0.00004480413,0.0004385914,0.000007141531,0.000474638,0.000004688225],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004961414,"about_ca_system_score_gemma":0.00004173298,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0003062783,"about_ca_topic_score_gemma":0.001337152,"domain_scores_codex":[0.9983676,0.000321009,0.0006116577,0.0002675231,0.0001464056,0.0002857693],"domain_scores_gemma":[0.9980379,0.0002647669,0.000116413,0.001377111,0.0001238252,0.00007998982],"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":[0.00002613778,0.0003345912,0.00003133422,0.00006959093,0.00003734522,3.657421e-7,0.0006315497,0.9918017,0.00200459,0.0004887779,0.00003657798,0.004537384],"study_design_scores_gemma":[0.000809387,0.00007708374,0.00005102327,0.00006255483,0.00003130854,0.000005326576,0.0005997797,0.9966477,0.0005675902,0.0001574109,0.0006985496,0.0002922998],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01767217,0.000198111,0.9786741,0.0001529068,0.0005504483,0.001328717,0.0007175537,0.0005895348,0.0001165347],"genre_scores_gemma":[0.9826794,0.0002397583,0.01402306,0.00003830719,0.00002202625,0.001943469,0.0007168127,0.0001163167,0.0002208268],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9650072,"threshold_uncertainty_score":0.9999525,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03521463453631516,"score_gpt":0.2641024180561941,"score_spread":0.2288877835198789,"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."}}