{"id":"W4388520051","doi":"10.1109/ojvt.2023.3331185","title":"Machine Learning-Based Self-Interference Cancellation for Full-Duplex Radio: Approaches, Open Challenges, and Future Research Directions","year":2023,"lang":"en","type":"article","venue":"IEEE Open Journal of Vehicular Technology","topic":"Full-Duplex Wireless Communications","field":"Engineering","cited_by":28,"is_retracted":false,"has_abstract":true,"ca_institutions":"Huawei Technologies (Canada); Memorial University of Newfoundland","funders":"","keywords":"Computer science; Single antenna interference cancellation; Wireless; Interference (communication); Computer engineering; Electronic engineering; Key (lock); Spectral efficiency; Overhead (engineering); Duplex (building); Open research; Computational complexity theory; Telecommunications; Engineering; Algorithm; 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":[0.002620521,0.0007801816,0.001144696,0.001060019,0.0003491467,0.002451485,0.001663162,0.001922205,0.002436828],"category_scores_gemma":[0.004087668,0.0004293114,0.0005829792,0.00144694,0.001425549,0.003869657,0.001136735,0.002771143,0.001121901],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007481135,"about_ca_system_score_gemma":0.0009785383,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001181276,"about_ca_topic_score_gemma":0.00107228,"domain_scores_codex":[0.9992524,0.0002461164,0.00004609063,0.0001521635,0.0002390306,0.00006414301],"domain_scores_gemma":[0.9951106,0.003441162,0.0001887442,0.0002886976,0.0008166012,0.0001542174],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0001098923,0.0003423812,0.001655259,0.00190015,0.0001542203,0.0001788541,0.0003046582,0.07173153,0.003188122,0.09603274,0.01025446,0.8141478],"study_design_scores_gemma":[0.0000387163,0.0003998712,0.001477648,0.000992425,0.0000743157,0.0004159597,0.0006209789,0.6953673,0.005158788,0.2024338,0.09285565,0.0001644109],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"review","genre_scores_codex":[0.01120031,0.403591,0.5572775,0.01359555,0.0009443086,0.00006749419,0.00008892452,0.0005555251,0.01267932],"genre_scores_gemma":[0.294932,0.3998236,0.2840422,0.003090546,0.005358983,0.0001919918,0.0003502799,0.0001927001,0.01201776],"genre_candidate":"review","genre_consensus":null,"teacher_disagreement_score":0.002620521,"threshold_uncertainty_score":0.0138588,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1183643174914587,"score_gpt":0.3278546221854314,"score_spread":0.2094903046939727,"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."}}