{"id":"W2796862611","doi":"10.1109/tcomm.2018.2854613","title":"Unified Analysis and Optimization of D2D Communications in Cellular Networks Over Fading Channels","year":2018,"lang":"en","type":"article","venue":"IEEE Transactions on Communications","topic":"Advanced MIMO Systems Optimization","field":"Engineering","cited_by":25,"is_retracted":false,"has_abstract":true,"ca_institutions":"Institut National de la Recherche Scientifique","funders":"","keywords":"Fading; Cellular network; Telecommunications link; Computer science; Stochastic geometry; Power control; Spectral efficiency; Transmitter power output; Cellular communication; Interference (communication); Electronic engineering; Signal-to-noise ratio (imaging); Computer network; Telecommunications; Power (physics); Base station; Engineering; Transmitter; Mathematics; Channel (broadcasting); Statistics","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":[0.0008828982,0.001247666,0.0008818381,0.0007854245,0.0004170879,0.00144554,0.0007769483,0.0009314123,0.001116214],"category_scores_gemma":[0.002908177,0.0004531453,0.0005918172,0.001199359,0.001156579,0.001209695,0.001538192,0.0007380634,0.0001859717],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001602479,"about_ca_system_score_gemma":0.001099865,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005545815,"about_ca_topic_score_gemma":0.003237688,"domain_scores_codex":[0.9993486,0.0002433138,0.00002195405,0.00009155645,0.000181241,0.0001133737],"domain_scores_gemma":[0.9989791,0.0006824077,0.0001178689,0.00005021026,0.0001230212,0.00004735247],"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":[0.000009998505,0.000006393254,0.0002060036,0.00002426253,0.00001078918,0.00003699202,0.00001562679,0.9769468,0.0005210255,0.01890501,0.000239185,0.003077977],"study_design_scores_gemma":[0.000001600051,0.000009274141,0.00008376303,0.000002855124,0.000003920767,0.000009313262,0.00001007788,0.9940944,0.0001288567,0.005453034,0.0001994972,0.000003399048],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.0227798,0.0008684796,0.9696139,0.000334798,0.00004048502,0.00002545605,0.0001151436,0.00005822641,0.006163707],"genre_scores_gemma":[0.9624553,0.00206577,0.03142472,0.0001373016,0.00009082445,0.0001215488,0.0001181553,0.00005649635,0.003529906],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.005545815,"threshold_uncertainty_score":0.0116269,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02322072589546916,"score_gpt":0.2619273742864944,"score_spread":0.2387066483910252,"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."}}