{"id":"W4200596779","doi":"10.3390/electronics10243061","title":"Resource Allocation in NOMA-Assisted Ambient Backscatter Communication System","year":2021,"lang":"en","type":"article","venue":"Electronics","topic":"Advanced Wireless Communication Technologies","field":"Engineering","cited_by":7,"is_retracted":false,"has_abstract":true,"ca_institutions":"École de Technologie Supérieure; Université du Québec à Montréal","funders":"","keywords":"Decoding methods; Backscatter (email); Benchmark (surveying); Noma; Computer science; Reflection (computer programming); Mathematical optimization; Resource allocation; Optimization problem; Computational complexity theory; Algorithm; Mathematics; Telecommunications; Wireless; Telecommunications link; Computer network; Geography","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":[0.0007656388,0.0009685023,0.0009691313,0.000396396,0.0005575641,0.0009642733,0.0007024063,0.0006289143,0.001318666],"category_scores_gemma":[0.001402335,0.00031102,0.0003738763,0.0006675352,0.0006143508,0.0009876917,0.0009868508,0.0005692865,0.0002444781],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005925554,"about_ca_system_score_gemma":0.0009341162,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002809194,"about_ca_topic_score_gemma":0.003064776,"domain_scores_codex":[0.9990884,0.0004089973,0.00003023386,0.0001283459,0.0001654997,0.000178408],"domain_scores_gemma":[0.9994467,0.0003082125,0.00006989008,0.00003903935,0.0001024159,0.00003369934],"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":[0.0001661672,0.00005628432,0.000687551,0.0000979399,0.00004729371,0.0002095045,0.00006531836,0.9443569,0.006204008,0.01173853,0.001215671,0.03515484],"study_design_scores_gemma":[0.00001661387,0.00005341112,0.0001370015,0.000004956333,0.00001359419,0.00006205188,0.00002225486,0.9962167,0.0007705943,0.002371916,0.00032233,0.000008616048],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.0620015,0.001147167,0.929699,0.0002879915,0.00007272723,0.00006772939,0.00006483105,0.0001984415,0.00646056],"genre_scores_gemma":[0.9311051,0.0004139351,0.06594012,0.000116822,0.00004137115,0.0001054279,0.00004085253,0.00002467646,0.002211597],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002809194,"threshold_uncertainty_score":0.005585611,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.009666667565935679,"score_gpt":0.2206375634991212,"score_spread":0.2109708959331855,"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."}}