{"id":"W4249025297","doi":"10.1109/glocom.2014.7417509","title":"Enhancing the Performance of Amplify-and-Forward Cognitive Relay Networks: A Multiple-Relay Scenario","year":2014,"lang":"en","type":"article","venue":"2015 IEEE Global Communications Conference (GLOBECOM)","topic":"Cognitive Radio Networks and Spectrum Sensing","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Concordia University","funders":"","keywords":"Underlay; Relay; Cognitive radio; Computer science; Transmitter; Interference (communication); Node (physics); Throughput; Transmission (telecommunications); Transmitter power output; Optimization problem; Computer network; Constraint (computer-aided design); Signal-to-noise ratio (imaging); Cognitive network; Mathematical optimization; Power (physics); Telecommunications; Engineering; Wireless; Algorithm; Mathematics; Channel (broadcasting)","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":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.001237349,0.0003354526,0.0004648911,0.00007146342,0.0008566131,0.000300729,0.00240875,0.0001652902,0.000009476734],"category_scores_gemma":[0.0003031099,0.0002746692,0.000119179,0.0007207944,0.0007043036,0.000523461,0.001054322,0.0006303688,0.00003731113],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000116039,"about_ca_system_score_gemma":0.0002077127,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0004556619,"about_ca_topic_score_gemma":0.002448529,"domain_scores_codex":[0.9972467,0.0006503807,0.0006751514,0.0005041231,0.0003534443,0.0005701716],"domain_scores_gemma":[0.9950486,0.00155667,0.0004422203,0.002131508,0.0006322699,0.0001887342],"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":[0.0002289048,0.000747373,0.06360327,0.000123067,0.0006318091,0.00000633944,0.004469425,0.01175422,0.0008995048,0.3084329,0.002969235,0.606134],"study_design_scores_gemma":[0.0007043715,0.0001712594,0.01477182,0.0003991328,0.00006482549,0.00005600187,0.0002395927,0.9799222,0.0002596237,0.0005678146,0.002453659,0.0003897557],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2148239,0.001531689,0.7731284,0.002315693,0.0004488428,0.0005769035,0.00002461323,0.0001801454,0.006969817],"genre_scores_gemma":[0.987325,0.001564257,0.01044497,0.0004666604,0.00008819919,0.00003024647,0.00002019534,0.00001382964,0.00004662343],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9681679,"threshold_uncertainty_score":0.9999706,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02389291400847128,"score_gpt":0.2698477933701968,"score_spread":0.2459548793617255,"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."}}