{"id":"W2288813195","doi":"10.1109/glocomw.2015.7414184","title":"Low Complexity Techniques for SCMA Detection","year":2015,"lang":"en","type":"article","venue":"","topic":"Advanced Wireless Communication Technologies","field":"Engineering","cited_by":104,"is_retracted":false,"has_abstract":true,"ca_institutions":"Huawei Technologies (Canada)","funders":"","keywords":"Codebook; Computer science; Decoding methods; Computational complexity theory; Code word; Message passing; Reduction (mathematics); Algorithm; Code (set theory); Multiplexing; Theoretical computer science; Computer engineering; Parallel computing; Telecommunications; Mathematics","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":[0.000501251,0.001206257,0.0005793798,0.001016373,0.0004839607,0.001050928,0.0009466558,0.0007890282,0.003638771],"category_scores_gemma":[0.004357903,0.0003867268,0.0006311935,0.001067405,0.000581671,0.001148551,0.0008137352,0.001682497,0.002065914],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006680879,"about_ca_system_score_gemma":0.0009105114,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0007785648,"about_ca_topic_score_gemma":0.001386735,"domain_scores_codex":[0.9985759,0.0002278426,0.00005978586,0.0001161039,0.0009599203,0.00006037775],"domain_scores_gemma":[0.9978549,0.00120701,0.0001817193,0.0003138735,0.0004069227,0.00003554609],"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":[0.0002538903,0.0001438766,0.0009444208,0.0005875935,0.0001066132,0.0002875264,0.0003474537,0.07176051,0.18622,0.1321454,0.005086123,0.6021166],"study_design_scores_gemma":[0.00004340821,0.0003019703,0.0005868668,0.00008557849,0.00005842645,0.001107084,0.00005965356,0.8406873,0.1005037,0.03306829,0.02343267,0.00006506924],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.001864575,0.0003545822,0.9957675,0.00008663844,0.00003631112,0.00002691705,0.00001707493,0.0001617309,0.001684654],"genre_scores_gemma":[0.111889,0.001081571,0.8812572,0.0001847646,0.0001795208,0.0001481833,0.0001520751,0.0000987089,0.005008995],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.003638771,"threshold_uncertainty_score":0.01217294,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0674419449922478,"score_gpt":0.2876614432139348,"score_spread":0.220219498221687,"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."}}