{"id":"W3176534971","doi":"10.1109/access.2021.3090965","title":"cDERSA: Cognitive D2D Enabled Relay Selection Algorithm to Mitigate Blind-Spots in 5G Cellular Networks","year":2021,"lang":"en","type":"article","venue":"IEEE Access","topic":"Cooperative Communication and Network Coding","field":"Computer Science","cited_by":18,"is_retracted":false,"has_abstract":true,"ca_institutions":"École de Technologie Supérieure","funders":"Umm Al-Qura University","keywords":"Relay; Computer science; Cognitive radio; Computer network; Base station; Throughput; Cellular network; Selection algorithm; Blind spot; Wireless; Selection (genetic algorithm); Telecommunications; Artificial intelligence","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":[0.0004779358,0.0007540826,0.0006431754,0.0006237719,0.0008073651,0.0007180178,0.001369821,0.0007415413,0.00163469],"category_scores_gemma":[0.001093782,0.0001586456,0.0004615513,0.0005214151,0.0006107291,0.000530636,0.001038064,0.0007282721,0.0004201138],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006020269,"about_ca_system_score_gemma":0.001493085,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005895186,"about_ca_topic_score_gemma":0.007636842,"domain_scores_codex":[0.9995833,0.00009012817,0.0000231367,0.00008735397,0.0001192755,0.00009686702],"domain_scores_gemma":[0.9996763,0.00009946617,0.00004362337,0.00003422618,0.0001040598,0.0000422736],"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":[0.0007377958,0.0003317877,0.004092501,0.0002387856,0.0002248537,0.0007831361,0.0003608545,0.5143732,0.03440244,0.01781581,0.01693719,0.4097016],"study_design_scores_gemma":[0.00008965798,0.0003305126,0.0006050783,0.00001703958,0.00004702446,0.0003690077,0.00006492354,0.983306,0.006013288,0.003300918,0.005826605,0.00002999909],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.06029509,0.001989461,0.9253173,0.0005561078,0.0003236178,0.000240879,0.000166771,0.001599803,0.009511058],"genre_scores_gemma":[0.8752539,0.0005583726,0.1171257,0.0003576356,0.000065679,0.0002107997,0.0002165947,0.00002829869,0.006183072],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.005895186,"threshold_uncertainty_score":0.01172179,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.044190714143861,"score_gpt":0.315745996433372,"score_spread":0.271555282289511,"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."}}