{"id":"W771759389","doi":"10.1007/s11235-015-0058-x","title":"Cooperative multiple access in cognitive radios to enhance QoS for cell edge primary users: asynchronous algorithm and convergence","year":2015,"lang":"en","type":"article","venue":"Telecommunication Systems","topic":"Cognitive Radio Networks and Spectrum Sensing","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of British Columbia","funders":"","keywords":"Computer science; Cognitive radio; Quality of service; Computer network; Asynchronous communication; Enhanced Data Rates for GSM Evolution; Maximization; Algorithm; Mathematical optimization; Telecommunications; Wireless; Mathematics","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":[0.0008016783,0.0001974598,0.0003533486,0.0001554134,0.0001690655,0.0003737585,0.0008495855,0.00007843526,0.000001228555],"category_scores_gemma":[0.0001519773,0.0002052552,0.00003207523,0.0005550224,0.00006291903,0.0007925845,0.0004216178,0.0001804356,0.00001504204],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002499885,"about_ca_system_score_gemma":0.0001961054,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0003116428,"about_ca_topic_score_gemma":0.0001728092,"domain_scores_codex":[0.9982396,0.000342988,0.0004133719,0.0004838889,0.000185172,0.0003349728],"domain_scores_gemma":[0.9977461,0.000785874,0.0001785059,0.0005911293,0.0005019659,0.0001964946],"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":[0.0004542698,0.001331551,0.01780403,0.0004250875,0.0002164191,0.00003864301,0.05028847,0.007468126,0.005206876,0.002537504,0.008214553,0.9060144],"study_design_scores_gemma":[0.002702914,0.0004702259,0.005141933,0.0004864022,0.00001771149,0.0000580416,0.001797052,0.9731143,0.009609724,0.00008468975,0.005732821,0.0007841776],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.06822269,0.002603125,0.9252622,0.0002951534,0.0004370463,0.001905545,0.00001915782,0.00009447319,0.001160601],"genre_scores_gemma":[0.9744564,0.0002480966,0.02442784,0.0003207478,0.0001295448,0.0002779491,0.00003072653,0.00001809084,0.00009061296],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9656462,"threshold_uncertainty_score":0.8370063,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04279724462638288,"score_gpt":0.3152881808405191,"score_spread":0.2724909362141362,"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."}}