{"id":"W2941720294","doi":"10.1109/access.2019.2910535","title":"Low Complexity Polar Decoder for 5G Embb Control Channel","year":2019,"lang":"en","type":"article","venue":"IEEE Access","topic":"Error Correcting Code Techniques","field":"Computer Science","cited_by":11,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"Higher Education Discipline Innovation Project; National Natural Science Foundation of China","keywords":"Computer science; Decoding methods; Computational complexity theory; Belief propagation; Algorithm; Error detection and correction; List decoding; Node (physics); Bit error rate; Concatenated error correction code; Sequential decoding; Polar code; Communication complexity; Linear network coding; Block code; Theoretical computer science; Computer network","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":[0.0003200211,0.0006039131,0.0004114515,0.0004274038,0.0005252803,0.0007233167,0.0004179986,0.0006370798,0.003251933],"category_scores_gemma":[0.00100515,0.000179146,0.0002056977,0.0003845563,0.0004372379,0.0006602416,0.0007118586,0.0008157421,0.0009056001],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000468038,"about_ca_system_score_gemma":0.00140316,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002774511,"about_ca_topic_score_gemma":0.004088297,"domain_scores_codex":[0.9996027,0.00009035463,0.00001685115,0.00006086033,0.0001816836,0.00004753256],"domain_scores_gemma":[0.9996074,0.0001226823,0.00004016243,0.00004479794,0.000167195,0.00001780035],"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":[0.001256034,0.0001685513,0.001958138,0.0005828769,0.00009781652,0.001196844,0.0003546733,0.345,0.1396935,0.149902,0.01376579,0.3460238],"study_design_scores_gemma":[0.00007576067,0.0001543208,0.0002634807,0.00003785796,0.00003497098,0.0004238544,0.00003652017,0.9474693,0.03599422,0.009281427,0.00619727,0.0000310751],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02475633,0.0009822814,0.9623189,0.0005243095,0.0001747235,0.000105426,0.0002086224,0.0007838058,0.01014549],"genre_scores_gemma":[0.736782,0.001646391,0.2467271,0.0005689623,0.0001375732,0.0001457604,0.0004823933,0.00006993548,0.01343999],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.003251933,"threshold_uncertainty_score":0.0108788,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04832602438044618,"score_gpt":0.327832548875133,"score_spread":0.2795065244946868,"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."}}