{"id":"W4389799491","doi":"10.1109/comst.2023.3340099","title":"A Survey on Model-Based, Heuristic, and Machine Learning Optimization Approaches in RIS-Aided Wireless Networks","year":2023,"lang":"en","type":"article","venue":"IEEE Communications Surveys & Tutorials","topic":"Advanced Wireless Communication Technologies","field":"Engineering","cited_by":119,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Ottawa","funders":"Engineering and Physical Sciences Research Council; CHIST-ERA; National Science Foundation","keywords":"Computer science; Heuristic; Robustness (evolution); Machine learning; Artificial intelligence; Maximization; Optimization problem; Minification; Mathematical optimization; Algorithm","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":[0.004676927,0.0003107737,0.0004756034,0.0005076392,0.0003333449,0.0000893742,0.001251912,0.0002696544,0.000003395269],"category_scores_gemma":[0.001160827,0.0003544714,0.00004665037,0.001509057,0.0002873526,0.0002002519,0.0003223408,0.0008439189,0.0000160489],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001873817,"about_ca_system_score_gemma":0.00004600595,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002850775,"about_ca_topic_score_gemma":0.001157455,"domain_scores_codex":[0.9961832,0.002207419,0.0006595871,0.0003160545,0.0002001671,0.0004336328],"domain_scores_gemma":[0.9936703,0.003641515,0.0001629267,0.002328902,0.000118624,0.00007773378],"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":[0.00001070637,0.00005392162,0.005833143,0.00001844979,0.00002007831,5.839993e-7,0.0001088004,0.9818174,0.00008111646,0.0003475918,0.0001239635,0.01158428],"study_design_scores_gemma":[0.0004755876,0.00002247603,0.00896389,0.00006368013,0.000006802435,3.446697e-7,0.00004977492,0.9894502,0.0002654585,0.0001958352,0.0001775476,0.0003283809],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.03885128,0.001880014,0.9545956,0.0002464628,0.0005120886,0.0006553127,0.0001123496,0.002724776,0.0004221082],"genre_scores_gemma":[0.9829494,0.006801251,0.008632349,0.00001177205,0.00003828863,0.0003018741,0.001118486,0.0001000306,0.00004654588],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9459633,"threshold_uncertainty_score":0.9998907,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1217713894330166,"score_gpt":0.288330346801155,"score_spread":0.1665589573681385,"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."}}