{"id":"W3174042897","doi":"10.1109/lcomm.2021.3091807","title":"Coverage Characterization of STAR-RIS Networks: NOMA and OMA","year":2021,"lang":"en","type":"article","venue":"IEEE Communications Letters","topic":"Advanced Wireless Communication Technologies","field":"Engineering","cited_by":211,"is_retracted":false,"has_abstract":true,"ca_institutions":"Memorial University of Newfoundland","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Mathematical optimization; Resource allocation; Noma; Maximization; Convex optimization; Star (game theory); Transmission (telecommunications); Optimization problem; Constraint (computer-aided design); Regular polygon; Decoding methods; Range (aeronautics); Algorithm; Telecommunications link; Mathematics; Telecommunications; Computer network; Engineering","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":[],"consensus_categories":[],"category_scores_codex":[0.00006225371,0.0001113402,0.0001637221,0.00008508496,0.0001056974,0.00002296704,0.0006075429,0.00006793033,0.000008868515],"category_scores_gemma":[0.00003041438,0.0001369192,0.00003155017,0.0003331593,0.0001902223,0.0002103812,0.0002301661,0.0002274792,0.000003882108],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004327402,"about_ca_system_score_gemma":0.000008874543,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000003895858,"about_ca_topic_score_gemma":0.00001357371,"domain_scores_codex":[0.9993542,0.00006616715,0.0002725803,0.0001084205,0.00007046439,0.0001281138],"domain_scores_gemma":[0.997583,0.0001552123,0.00007971605,0.00208943,0.00006724628,0.00002536289],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.000002660956,0.00004557319,0.001099944,0.00003891543,0.00006087956,0.000001917482,0.0002586537,0.02405587,0.944894,0.001356224,0.0003056655,0.02787969],"study_design_scores_gemma":[0.001299691,0.00004438819,0.0564334,0.0003420412,0.00006786233,0.00005195423,0.0004963937,0.4685818,0.4081978,0.0007853367,0.06254473,0.00115461],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7280868,0.001509023,0.2661605,0.003333042,0.00009513339,0.0001187479,0.00002779748,0.0004271254,0.0002418553],"genre_scores_gemma":[0.9770163,0.009883506,0.0125491,0.0003170994,0.000009494542,0.00004381388,0.0001410009,0.00002717834,0.00001252782],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.5366963,"threshold_uncertainty_score":0.5583405,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01389661999900465,"score_gpt":0.2224621619787833,"score_spread":0.2085655419797787,"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."}}