{"id":"W2289285750","doi":"10.1109/glocom.2015.7417509","title":"Enhancing the Performance of Amplify-and-Forward Cognitive Relay Networks: A Multiple-Relay Scenario","year":2015,"lang":"en","type":"article","venue":"2015 IEEE Global Communications Conference (GLOBECOM)","topic":"Cognitive Radio Networks and Spectrum Sensing","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"Concordia University","funders":"","keywords":"Underlay; Relay; Cognitive radio; Computer science; Interference (communication); Transmitter; Node (physics); Throughput; Transmission (telecommunications); Transmitter power output; Computer network; Constraint (computer-aided design); Optimization problem; Cognitive network; Signal-to-noise ratio (imaging); Mathematical optimization; Power (physics); Telecommunications; Wireless; Engineering; 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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00168419,0.001687789,0.001030122,0.0004934262,0.000486538,0.001336469,0.0009845603,0.001042912,0.0007756659],"category_scores_gemma":[0.00465986,0.0002570101,0.0003676232,0.0005087696,0.0009194582,0.001234851,0.001239383,0.0006293278,0.0002109832],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006211717,"about_ca_system_score_gemma":0.0007702701,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001196024,"about_ca_topic_score_gemma":0.001273811,"domain_scores_codex":[0.9992253,0.0002978444,0.000019402,0.00009608995,0.0001888596,0.0001726127],"domain_scores_gemma":[0.9983777,0.001164522,0.000141886,0.00008625985,0.0001779193,0.00005171933],"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":[0.0003458676,0.0001524191,0.0005920628,0.0001490416,0.00007093051,0.0003352379,0.0001527473,0.92863,0.02043343,0.01443424,0.0004348212,0.03426925],"study_design_scores_gemma":[0.00002348711,0.0002165779,0.0002425765,0.000008448305,0.00003521696,0.0001237574,0.00004424257,0.9849197,0.006173558,0.007924805,0.0002727556,0.00001487071],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.240636,0.0009872329,0.7427934,0.0003396793,0.00005450866,0.0000571022,0.00005305771,0.0003237157,0.01475533],"genre_scores_gemma":[0.9845819,0.0002427052,0.01460179,0.00003506097,0.0000232966,0.00001754553,0.000007851262,0.00001066955,0.0004792009],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.001687789,"threshold_uncertainty_score":0.00890696,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04684814527069362,"score_gpt":0.2904714724238442,"score_spread":0.2436233271531506,"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."}}