{"id":"W2964066177","doi":"10.1109/tcomm.2018.2840705","title":"Full-duplex relay selection in cognitive underlay networks","year":2018,"lang":"en","type":"article","venue":"Qatar University QSpace (Qatar University)","topic":"Full-Duplex Wireless Communications","field":"Engineering","cited_by":18,"is_retracted":false,"has_abstract":true,"ca_institutions":"Institut National de la Recherche Scientifique; Université du Québec à Montréal","funders":"King Abdullah University of Science and Technology; Qualcomm","keywords":"Relay; Rayleigh fading; Underlay; Cognitive radio; Computer science; Fading; Diversity gain; Nakagami distribution; Interference (communication); Throughput; Spectral efficiency; Computer network; Signal-to-noise ratio (imaging); Topology (electrical circuits); Decoding methods; Electronic engineering; Telecommunications; Power (physics); Engineering; Wireless; Physics; Electrical engineering; 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":[0.0007015594,0.0005956423,0.000465412,0.0004844751,0.0004450185,0.0008802623,0.0006450149,0.0005134637,0.000643574],"category_scores_gemma":[0.002386778,0.0001968159,0.0003247794,0.0005255716,0.0009746407,0.0007715807,0.0007555021,0.0002989717,0.00009933273],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001006413,"about_ca_system_score_gemma":0.0005359407,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00505678,"about_ca_topic_score_gemma":0.003304553,"domain_scores_codex":[0.9996516,0.00009557461,0.000009428999,0.0000450469,0.0000824348,0.0001159917],"domain_scores_gemma":[0.998869,0.0006980161,0.0001707977,0.00006525439,0.00014776,0.00004916274],"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":[0.0001180085,0.00003979389,0.001525382,0.00006187792,0.00004412909,0.0003716561,0.0001825084,0.9652075,0.005692709,0.0186567,0.000302325,0.007797457],"study_design_scores_gemma":[0.000003596317,0.00004072109,0.0003904421,0.000003087547,0.00001197759,0.00006660422,0.00003707401,0.9953289,0.000790893,0.003204198,0.0001150103,0.000007622979],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.679245,0.001338353,0.3111929,0.0001515812,0.00003812668,0.00003655497,0.00009237367,0.0001619589,0.007743121],"genre_scores_gemma":[0.9967547,0.0001985202,0.002553393,0.00001441576,0.000007250654,0.000008579685,0.000008846159,0.000004410643,0.0004498266],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.00505678,"threshold_uncertainty_score":0.01005465,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01298724551005098,"score_gpt":0.1936295736182196,"score_spread":0.1806423281081687,"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."}}