{"id":"W2414419511","doi":"10.1109/tvt.2016.2578180","title":"Bidirectional AF Relaying With Underlay Spectrum Sharing in Cognitive Radio Networks","year":2016,"lang":"en","type":"article","venue":"IEEE Transactions on Vehicular Technology","topic":"Cooperative Communication and Network Coding","field":"Computer Science","cited_by":31,"is_retracted":false,"has_abstract":true,"ca_institutions":"Institut National de la Recherche Scientifique; Université du Québec à Montréal","funders":"","keywords":"Underlay; Cognitive radio; Relay; Quadrature amplitude modulation; Additive white Gaussian noise; Computer science; Upper and lower bounds; Transceiver; Electronic engineering; Topology (electrical circuits); Bit error rate; Telecommunications; Signal-to-noise ratio (imaging); Computer network; Mathematics; Decoding methods; Engineering; Power (physics); White noise; Wireless; Electrical engineering; Physics","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002076896,0.0001772459,0.0001900202,0.0006170616,0.0002857438,0.00004516701,0.0006356651,0.0001788197,0.00005083614],"category_scores_gemma":[0.000008929803,0.0001343248,0.00005445817,0.001490539,0.0001587463,0.0002861768,0.00001508064,0.0005073662,0.00002898673],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001858693,"about_ca_system_score_gemma":0.00005024104,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00000567695,"about_ca_topic_score_gemma":0.0003161615,"domain_scores_codex":[0.9987128,0.00007392874,0.0002212613,0.0005060931,0.0001480609,0.0003378935],"domain_scores_gemma":[0.9990498,0.0001784494,0.0000654453,0.0005851238,0.00007076899,0.00005037603],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0001992976,0.0007921336,0.005051521,0.00001090481,0.0003679438,0.0002339401,0.0004900859,0.07153916,0.008539907,0.08220983,0.00009631672,0.830469],"study_design_scores_gemma":[0.01301693,0.002223995,0.007943051,0.003412558,0.0001301451,0.001452101,0.000464165,0.7668067,0.1823866,0.01296455,0.006068418,0.003130889],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.04632421,0.0002353974,0.9471989,0.004990065,0.0001903914,0.0001938656,0.000001069525,0.0004861348,0.00037994],"genre_scores_gemma":[0.9962929,0.0006150845,0.002517852,0.000138374,0.00001916433,0.0001010843,4.795256e-7,0.00001683123,0.0002982498],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9499687,"threshold_uncertainty_score":0.5477605,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0238415128837675,"score_gpt":0.2529274496435531,"score_spread":0.2290859367597856,"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."}}