{"id":"W4245932276","doi":"10.32920/ryerson.14660871.v1","title":"Resource allocation strategies in cognitive radio D2D communication networks","year":2021,"lang":"en","type":"preprint","venue":"","topic":"Advanced MIMO Systems Optimization","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Subcarrier; Cognitive radio; Computer science; Resource allocation; Orthogonal frequency-division multiplexing; Radio resource management; Spectral efficiency; Cellular network; Computer network; Wireless; Distributed computing; Mathematical optimization; Wireless network; Channel (broadcasting); Telecommunications; 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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007409705,0.0006558354,0.0005566669,0.0005347326,0.0005357221,0.001158202,0.000879483,0.0009026023,0.0009083941],"category_scores_gemma":[0.001745224,0.0003231394,0.0003078452,0.0007248027,0.001015835,0.0009705987,0.0009682014,0.0006027182,0.0002060471],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001153039,"about_ca_system_score_gemma":0.0009725133,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002314776,"about_ca_topic_score_gemma":0.001594132,"domain_scores_codex":[0.9994847,0.0001950081,0.0000238148,0.00008964244,0.0001084965,0.00009823489],"domain_scores_gemma":[0.9995503,0.0002809862,0.00005537931,0.00002622195,0.00006049796,0.00002655621],"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":[0.00005292462,0.00004587484,0.0003096072,0.0001243419,0.0000272921,0.0001204054,0.0001450693,0.7982219,0.003224851,0.1330658,0.001749083,0.06291273],"study_design_scores_gemma":[0.000009896396,0.0000322347,0.00005869102,0.000008630628,0.000005600066,0.00003754714,0.00003571958,0.9657266,0.0005859563,0.03219275,0.001298005,0.000008398009],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01748908,0.001376088,0.9735515,0.0003054647,0.00005155494,0.00005567426,0.00002277205,0.00006037594,0.007087578],"genre_scores_gemma":[0.8579012,0.001797409,0.1362859,0.0001802854,0.00006178085,0.0001833251,0.00003341478,0.0000284149,0.003528368],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002314776,"threshold_uncertainty_score":0.008365929,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0166869013608204,"score_gpt":0.253791190766413,"score_spread":0.2371042894055926,"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."}}