{"id":"W4414467816","doi":"10.1186/s13638-025-02509-1","title":"Intelligent power optimization for capacity maximization in IRS-assisted NOMA networks","year":2025,"lang":"en","type":"article","venue":"EURASIP Journal on Wireless Communications and Networking","topic":"Advanced Wireless Communication Technologies","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"Carleton University; University of Ottawa","funders":"Princess Nourah Bint Abdulrahman University; National Research Foundation of Korea; Chung-Ang University","keywords":"Maximization; Capacity optimization; Power (physics); Interference (communication); MATLAB; Limiting; Margin (machine learning); Power Balance; Capacity planning","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":[],"consensus_categories":[],"category_scores_codex":[0.0004375397,0.0002162769,0.000283064,0.0004166172,0.0005236336,0.0001432598,0.0008006505,0.0001744524,0.000005297238],"category_scores_gemma":[0.00004670651,0.00022834,0.00006732711,0.0007300065,0.0001330765,0.0002192126,0.0002032222,0.0007712045,5.97538e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002665981,"about_ca_system_score_gemma":0.0000208623,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000002279604,"about_ca_topic_score_gemma":0.00004068575,"domain_scores_codex":[0.9986822,0.0001434998,0.0006207644,0.0001656576,0.00009439966,0.0002934972],"domain_scores_gemma":[0.9980132,0.0006639888,0.0001800796,0.0009546881,0.0001354017,0.00005266651],"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":[0.00002099864,0.00005792857,0.001157095,0.00001557664,0.00003587252,5.19267e-7,0.0001029664,0.7709337,0.00005125069,0.005725238,0.0001091229,0.2217897],"study_design_scores_gemma":[0.0004763983,0.00004098568,0.001194788,0.0004955764,0.00001220048,0.00001165352,0.0001805177,0.987215,0.0001155177,0.001009819,0.009036042,0.0002114887],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.0185571,0.007755922,0.9703256,0.0008977604,0.000429076,0.0003985773,0.000003176874,0.0002973362,0.001335388],"genre_scores_gemma":[0.9308444,0.03681202,0.03201876,0.0001182764,0.00003433284,0.00009366265,0.0000264238,0.00003657435,0.00001558311],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9383069,"threshold_uncertainty_score":0.9311435,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03338554821518708,"score_gpt":0.2714873011460253,"score_spread":0.2381017529308382,"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."}}