{"id":"W2725444302","doi":"10.1007/978-3-319-57690-9_6","title":"Maximum Likelihood Self-Interference Cancellation","year":2017,"lang":"en","type":"book-chapter","venue":"Wireless networks","topic":"Full-Duplex Wireless Communications","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":false,"ca_institutions":"McGill University","funders":"","keywords":"Computer science; Estimator; Interference (communication); Algorithm; Transceiver; Channel (broadcasting); Convergence (economics); Noise (video); Iterative method; Single antenna interference cancellation; Signal-to-noise ratio (imaging); Process (computing); Phase (matter); Gaussian noise; Maximum likelihood; Mathematical optimization; Telecommunications; Mathematics; Statistics; Wireless; 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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0001292972,0.0006653509,0.000646288,0.0001423934,0.000318436,0.0001991948,0.001367284,0.0009027871,0.0003284172],"category_scores_gemma":[0.000005587587,0.0007772009,0.0002098443,0.00002762855,0.0001204779,0.0001698927,0.0002718723,0.001420243,0.0003562434],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003665178,"about_ca_system_score_gemma":0.00008137253,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00003717031,"about_ca_topic_score_gemma":0.0007466664,"domain_scores_codex":[0.9981468,0.00002011117,0.000548768,0.0004616466,0.0002622426,0.0005604311],"domain_scores_gemma":[0.996891,0.0001184179,0.0002936809,0.002365983,0.0001401552,0.0001908102],"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":[0.00002016958,0.000033988,0.00004588702,0.0003136644,0.0006157027,0.00003888792,0.0003615927,0.7322035,0.00005767436,0.02244649,0.02687172,0.2169907],"study_design_scores_gemma":[0.0002619722,0.00002342988,0.00005467872,0.0008323656,0.0001069022,0.00001201781,0.000004128025,0.8359725,0.00002136575,0.0007150135,0.1611393,0.0008563282],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"other","genre_gemma":"empirical","genre_scores_codex":[0.001100818,0.01090263,0.02823141,0.0001736756,0.00341684,0.0009130319,0.0000997469,0.002864486,0.9522974],"genre_scores_gemma":[0.8697509,0.01874484,0.001256372,0.00006321222,0.00175407,0.0001290885,0.0005305994,0.0005978381,0.1071731],"genre_candidate":"other","genre_consensus":null,"teacher_disagreement_score":0.8686501,"threshold_uncertainty_score":0.9994679,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01327268500929385,"score_gpt":0.2112360220045177,"score_spread":0.1979633369952238,"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."}}