{"id":"W4405907600","doi":"10.1109/wf-iot62078.2024.10811339","title":"Energy-Efficient Clustering and Power Allocation in RSMA-Enabled IoT Networks with Finite Blocklength Coding and Hardware Impairments","year":2024,"lang":"en","type":"article","venue":"","topic":"Molecular Communication and Nanonetworks","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"École de Technologie Supérieure","funders":"","keywords":"Computer science; Cluster analysis; Internet of Things; Coding (social sciences); Ultra low power; Power (physics); Computer architecture; Embedded system; Computer hardware; Power consumption; Mathematics; Physics; Artificial intelligence","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":[0.0004953362,0.0004128662,0.0004460185,0.0002806671,0.0003474382,0.00047222,0.0008013429,0.0003961089,0.0005507329],"category_scores_gemma":[0.001090306,0.00022225,0.0002512107,0.0003622807,0.0007273043,0.0007178259,0.0005426389,0.0003889628,0.0001205177],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009593469,"about_ca_system_score_gemma":0.0007339952,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004286541,"about_ca_topic_score_gemma":0.004743284,"domain_scores_codex":[0.99971,0.00009121405,0.000009097398,0.00005753796,0.00007200145,0.00006012706],"domain_scores_gemma":[0.9995359,0.0002228646,0.0001023278,0.00003701504,0.00006774221,0.00003402962],"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":[0.00003349146,0.0000205121,0.000239858,0.00001622061,0.00001073232,0.00003928444,0.00003211493,0.9815996,0.003528284,0.006434295,0.0001737733,0.007871792],"study_design_scores_gemma":[0.000001764835,0.00001296582,0.00004972773,0.000001011887,0.000001981534,0.000006424825,0.000006161165,0.9979462,0.0004264197,0.001463803,0.00008134937,0.000002137288],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1377608,0.0003547989,0.8569136,0.0002183569,0.0000307181,0.00004655377,0.00003612455,0.0001618728,0.004477119],"genre_scores_gemma":[0.9762531,0.00008529333,0.02249828,0.00003133953,0.000006722661,0.00002613175,0.000013196,0.000009402057,0.001076555],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.004286541,"threshold_uncertainty_score":0.008523166,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.005655208096347322,"score_gpt":0.1933265129375779,"score_spread":0.1876713048412306,"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."}}