{"id":"W2729768891","doi":"10.1007/978-3-319-57690-9_5","title":"Widely-Linear Subspace-Based 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; Baseband; Signal subspace; Subspace topology; MIMO; Single antenna interference cancellation; Algorithm; Covariance; Interference (communication); Channel (broadcasting); Transmitter; Dimension (graph theory); SIGNAL (programming language); Telecommunications; Mathematics; Bandwidth (computing); Artificial intelligence; Noise (video); 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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0001783599,0.0007878602,0.0007853073,0.0002025225,0.0003612449,0.0001685713,0.001326692,0.001049451,0.0003876769],"category_scores_gemma":[0.0000104674,0.0009067995,0.0002957569,0.00004206516,0.0001830306,0.0001614034,0.0001702425,0.001549852,0.0003376605],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004054191,"about_ca_system_score_gemma":0.0001308195,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002911429,"about_ca_topic_score_gemma":0.000575471,"domain_scores_codex":[0.9978965,0.00003305281,0.0006369549,0.0005309458,0.0003368264,0.0005657179],"domain_scores_gemma":[0.9963304,0.0002283944,0.0003817515,0.002654458,0.0001984223,0.0002065401],"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":[0.00001791203,0.00001647049,0.00003784402,0.0001322724,0.0001945899,0.00001305291,0.00007415876,0.9727196,0.00002147684,0.007612533,0.007530005,0.01163008],"study_design_scores_gemma":[0.000393232,0.00002677773,0.00003869936,0.0008165167,0.0001070149,0.000003928147,0.000002947652,0.8730958,0.00004668965,0.00007098432,0.1246107,0.0007867841],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"other","genre_gemma":"empirical","genre_scores_codex":[0.004567616,0.01216656,0.1167636,0.0006550839,0.005219079,0.002133535,0.0002081326,0.006079406,0.852207],"genre_scores_gemma":[0.8831221,0.005423902,0.002318281,0.00008490812,0.001514578,0.0001189904,0.0007951235,0.0005956945,0.1060264],"genre_candidate":"other","genre_consensus":null,"teacher_disagreement_score":0.8785545,"threshold_uncertainty_score":0.9993383,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02199178121770636,"score_gpt":0.2247413527726839,"score_spread":0.2027495715549775,"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."}}