{"id":"W3123137409","doi":"10.1109/tit.2021.3053166","title":"Rate Splitting and Successive Decoding for Gaussian Interference Channels","year":2021,"lang":"en","type":"article","venue":"IEEE Transactions on Information Theory","topic":"Wireless Communication Security Techniques","field":"Engineering","cited_by":7,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Decoding methods; Interference (communication); Gaussian; List decoding; Mathematics; Sequential decoding; Coding (social sciences); Algorithm; Dirty paper coding; Joint (building); Computer science; Channel (broadcasting); Topology (electrical circuits); Telecommunications; Statistics; Block code; Combinatorics; Concatenated error correction code; Precoding; MIMO; Physics","routes":{"ca_aff":true,"ca_fund":true,"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":[],"consensus_categories":[],"category_scores_codex":[0.0003168886,0.0001108548,0.0001130772,0.0001518581,0.0001731499,0.0001276378,0.0001172397,0.00007320444,0.00005363385],"category_scores_gemma":[0.00002758066,0.0001226624,0.00004490775,0.0001396842,0.00003420209,0.0009822443,0.000002221714,0.0001797908,0.00001562275],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004894734,"about_ca_system_score_gemma":0.00001799931,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000001315385,"about_ca_topic_score_gemma":0.000003705561,"domain_scores_codex":[0.9994035,0.00005324333,0.0002831066,0.00007104254,0.00005641149,0.0001326816],"domain_scores_gemma":[0.99918,0.0003840296,0.00005313456,0.0002273298,0.0001092215,0.00004634048],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0001398784,0.00006507999,0.000004576327,0.0008108275,0.0001954489,0.000001536299,0.01950149,0.06326586,0.008296236,0.1278158,0.0003597473,0.7795435],"study_design_scores_gemma":[0.000626713,0.00004969593,0.0000162953,0.0003170952,0.00003169458,0.00001714571,0.002832414,0.3116406,0.6684175,0.01280033,0.002858785,0.0003917467],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.009624668,0.00005298698,0.9870929,0.0001171754,0.0002693819,0.0001929709,0.00003455647,0.0004175997,0.00219774],"genre_scores_gemma":[0.9945045,0.0001895807,0.004872251,0.000168636,0.00001371853,0.0001631719,0.00001538024,0.00001355387,0.00005920091],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9848799,"threshold_uncertainty_score":0.5002026,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01326336738752988,"score_gpt":0.2452581685780593,"score_spread":0.2319948011905294,"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."}}