{"id":"W2399609796","doi":"10.1109/tvt.2016.2570801","title":"Cognitive Coded Cooperation in Underlay Spectrum-Sharing Networks Under Interference Power Constraints","year":2016,"lang":"en","type":"article","venue":"IEEE Transactions on Vehicular Technology","topic":"Cooperative Communication and Network Coding","field":"Computer Science","cited_by":17,"is_retracted":false,"has_abstract":true,"ca_institutions":"Concordia University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Cognitive radio; Computer science; Phase-shift keying; Rayleigh fading; Underlay; Spectral efficiency; Pairwise error probability; Bit error rate; Electronic engineering; Interference (communication); Signal-to-noise ratio (imaging); Fading; Computer network; Telecommunications; Wireless; Decoding methods; Channel (broadcasting); 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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001557023,0.0006997955,0.000838479,0.0005840134,0.0006330943,0.001300453,0.001067523,0.0008017118,0.0006028383],"category_scores_gemma":[0.005491185,0.0003929156,0.0003410568,0.0008680517,0.00157925,0.001107633,0.001209257,0.0005885852,0.0001124152],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001414849,"about_ca_system_score_gemma":0.001206043,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.006212666,"about_ca_topic_score_gemma":0.005171682,"domain_scores_codex":[0.9989749,0.0003937266,0.00002646128,0.0001016031,0.0002559141,0.00024743],"domain_scores_gemma":[0.9957071,0.003223067,0.0004041888,0.0001715483,0.0004018702,0.00009224375],"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":[0.00009138713,0.00002228731,0.0004899974,0.00006920415,0.00003643243,0.0005964039,0.0002586332,0.9537296,0.002451614,0.03423243,0.0003451859,0.007676876],"study_design_scores_gemma":[0.000006497905,0.00002559479,0.0001411098,0.000005888827,0.00001234039,0.00008593861,0.00004018638,0.9862671,0.0004654026,0.01278239,0.0001580831,0.000009458401],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3648692,0.00137414,0.6210173,0.0003523858,0.00004723833,0.00005332728,0.0001228613,0.0002142525,0.0119492],"genre_scores_gemma":[0.9929714,0.0002432737,0.00582916,0.00002968823,0.00001166503,0.00002437882,0.00001227255,0.000007726392,0.0008705437],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.006212666,"threshold_uncertainty_score":0.012353,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02681955275368228,"score_gpt":0.2692242868571702,"score_spread":0.2424047341034879,"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."}}