{"id":"W4392024454","doi":"10.1109/jiot.2024.3368516","title":"Knowledge-Driven Resource Allocation for Wireless Networks: A WMMSE Unrolled Graph Neural Network Approach","year":2024,"lang":"en","type":"article","venue":"IEEE Internet of Things Journal","topic":"Energy Efficient Wireless Sensor Networks","field":"Computer Science","cited_by":26,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"Chinese Polar Environment Comprehensive Investigation and Assessment Programmes","keywords":"Computer science; Scalability; Wireless network; Distributed computing; Network topology; Wireless; Computer network","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":[0.0004031451,0.000633174,0.0005502365,0.0003603849,0.0003012961,0.0006411059,0.00142172,0.0008895107,0.001372095],"category_scores_gemma":[0.001462361,0.0003554927,0.0003403116,0.000477253,0.000658889,0.001769377,0.0008911093,0.001146031,0.0002098835],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007339505,"about_ca_system_score_gemma":0.0007584304,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004878109,"about_ca_topic_score_gemma":0.00750615,"domain_scores_codex":[0.9997991,0.00004545916,0.000009860228,0.00006937634,0.00004347621,0.00003275978],"domain_scores_gemma":[0.999693,0.0001493156,0.0000344244,0.00005016991,0.00005407441,0.00001899795],"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":[0.00003105328,0.00003758748,0.0002457706,0.00003435148,0.00002104224,0.00004245278,0.00003520882,0.9311656,0.00299442,0.008084583,0.0008170903,0.05649085],"study_design_scores_gemma":[0.000001487472,0.000006933404,0.00002261806,0.000001241355,0.000001651047,0.000004724587,0.000003040792,0.9961844,0.0003782077,0.003222717,0.0001712611,0.000001729571],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.0154272,0.0001887175,0.9820622,0.0002221205,0.00003755446,0.00002332722,0.00003749281,0.0003393888,0.001662032],"genre_scores_gemma":[0.7347914,0.0002932329,0.2601132,0.0002846467,0.00005785457,0.0001173965,0.0001587883,0.0001083752,0.004075106],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.004878109,"threshold_uncertainty_score":0.009699404,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01711728597511903,"score_gpt":0.2515941807204362,"score_spread":0.2344768947453172,"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."}}