{"id":"W2104150212","doi":"10.1109/wcnc.2014.6952248","title":"Throughput maximization for cognitive radio networks using active cooperation and superposition coding","year":2014,"lang":"en","type":"article","venue":"","topic":"Cognitive Radio Networks and Spectrum Sensing","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"Institut National de la Recherche Scientifique; Université du Québec à Montréal","funders":"","keywords":"Cognitive radio; Computer science; Relay; Throughput; Maximization; Computer network; Linear network coding; Transmission (telecommunications); Node (physics); Mathematical optimization; Power (physics); Wireless; Telecommunications; Engineering; Mathematics; Network packet","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":[0.001809733,0.000853669,0.0006490254,0.000441949,0.0004721657,0.0009391024,0.0009160392,0.0006109242,0.0006955121],"category_scores_gemma":[0.002781219,0.0003088276,0.0006395003,0.0009089442,0.001579626,0.001139098,0.0009230227,0.0006882603,0.0001413441],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001455785,"about_ca_system_score_gemma":0.001288895,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001922475,"about_ca_topic_score_gemma":0.002045793,"domain_scores_codex":[0.9991077,0.000448013,0.00002356607,0.00006570177,0.0002264171,0.0001286485],"domain_scores_gemma":[0.9981678,0.001395337,0.000143602,0.0001032659,0.0001472545,0.00004263184],"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":[0.0001502046,0.00006612815,0.0003221303,0.00009990948,0.00005053779,0.0001343429,0.0001919738,0.8618029,0.01456054,0.09336012,0.00081137,0.02844985],"study_design_scores_gemma":[0.000009471187,0.00003342256,0.00005397958,0.000004312759,0.00001054953,0.00001854119,0.00001080665,0.9852663,0.001358973,0.0130294,0.0001982111,0.000006066249],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.05279543,0.0003543574,0.9416877,0.0002597109,0.00002658194,0.00003148548,0.00002882436,0.0001097785,0.004706065],"genre_scores_gemma":[0.9477023,0.0003792868,0.05049806,0.00006154325,0.00004140488,0.00007655077,0.00002450824,0.00002090803,0.00119544],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.001922475,"threshold_uncertainty_score":0.01056248,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01814671827131653,"score_gpt":0.2483330136735584,"score_spread":0.2301862954022419,"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."}}