{"id":"W3207692869","doi":"10.36227/techrxiv.15157944.v1","title":"Hybrid Cognitive-Radio NOMA with Blind Transmission Mode Identification and BER Constraints","year":2021,"lang":"en","type":"preprint","venue":"","topic":"Advanced Wireless Communication Technologies","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Western University","funders":"","keywords":"Computer science; Cognitive radio; Quality of service; Telecommunications link; Underlay; Computer network; Multiplexing; Throughput; Transmission (telecommunications); Single antenna interference cancellation; Context (archaeology); Spectrum management; Interference (communication); Noma; Wireless; Channel (broadcasting); Signal-to-noise ratio (imaging); Telecommunications","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":[0.001890588,0.001501479,0.00103893,0.0009927687,0.0005412413,0.001551383,0.001308054,0.0009733245,0.0006090262],"category_scores_gemma":[0.004396006,0.0003738051,0.0006396119,0.0008377678,0.001127083,0.001292809,0.00101379,0.0004469109,0.000125161],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008482416,"about_ca_system_score_gemma":0.001263476,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00536639,"about_ca_topic_score_gemma":0.005046319,"domain_scores_codex":[0.9981159,0.0007014318,0.00007038887,0.0001942502,0.0005295417,0.0003884328],"domain_scores_gemma":[0.9967732,0.001738793,0.0005915586,0.0003110138,0.0004829795,0.0001025071],"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":[0.0004220221,0.0001758307,0.001820035,0.0002081284,0.0001558538,0.000499976,0.00008198244,0.9514005,0.0133357,0.01626224,0.0003065463,0.01533108],"study_design_scores_gemma":[0.00001634082,0.0001286598,0.0003329726,0.0000052087,0.00003120145,0.0001132586,0.00001835886,0.9949194,0.002222281,0.002058716,0.0001370901,0.0000164467],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3378905,0.001038252,0.6507645,0.0001013121,0.0001006247,0.0001402534,0.0001316724,0.0003563107,0.009476655],"genre_scores_gemma":[0.9809863,0.000142804,0.01808324,0.0000310884,0.00001864064,0.00005356161,0.00001627927,0.000007877406,0.0006602459],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.00536639,"threshold_uncertainty_score":0.0106703,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01786765292206932,"score_gpt":0.2564001520706255,"score_spread":0.2385324991485562,"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."}}