{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001694787,0.0008097223,0.00056246,0.0005548359,0.0004453672,0.0007181711,0.0006192113,0.000485034,0.001160674],"category_scores_gemma":[0.005007898,0.0002599432,0.0005333442,0.000924276,0.001521879,0.001221846,0.0009619463,0.0009923753,0.0003099272],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007717428,"about_ca_system_score_gemma":0.001073942,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00133647,"about_ca_topic_score_gemma":0.001177509,"domain_scores_codex":[0.9984331,0.0005965413,0.00007060745,0.0001479325,0.0005483298,0.0002035877],"domain_scores_gemma":[0.9976652,0.00148665,0.000202501,0.0003135652,0.0002847324,0.00004740438],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0003175514,0.00005556964,0.0005807149,0.0002348733,0.0000582146,0.0002604121,0.0004627353,0.371404,0.03196897,0.4803977,0.001110647,0.1131487],"study_design_scores_gemma":[0.000030617,0.0001640594,0.0001871327,0.00003345367,0.00002999468,0.0003850088,0.00004446144,0.8932447,0.01543119,0.08695722,0.003452062,0.00004010502],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02630863,0.0008457222,0.9666529,0.0001355183,0.00003282675,0.0000343302,0.00003554689,0.0001451195,0.005809501],"genre_scores_gemma":[0.8064006,0.001725182,0.1886423,0.0001370209,0.0001006701,0.0001083011,0.00007271283,0.00005216502,0.002761092],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.001694787,"threshold_uncertainty_score":0.008962989,"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."}}