{"id":"W2042519026","doi":"10.1109/pimrc.2013.6666156","title":"Sparse code multiple access","year":2013,"lang":"en","type":"article","venue":"","topic":"Wireless Communication Networks Research","field":"Computer Science","cited_by":1293,"is_retracted":false,"has_abstract":true,"ca_institutions":"Huawei Technologies (Canada)","funders":"","keywords":"Codebook; Computer science; Code word; Code division multiple access; Algorithm; Code (set theory); Quadrature amplitude modulation; QAM; Heuristic; Multiplexing; Theoretical computer science; Set (abstract data type); Computer engineering; Decoding methods; Bit error rate; Telecommunications; Artificial intelligence","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":[0.0005205016,0.0006770404,0.001090018,0.001096275,0.000767991,0.001615993,0.001279422,0.001391702,0.01007231],"category_scores_gemma":[0.002469083,0.0002300202,0.0004662168,0.001919036,0.0006807058,0.001211541,0.002154294,0.001095677,0.0041723],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004518166,"about_ca_system_score_gemma":0.0009355493,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001265396,"about_ca_topic_score_gemma":0.001447595,"domain_scores_codex":[0.9986303,0.0003379766,0.00006410754,0.0002754925,0.0005155606,0.0001765295],"domain_scores_gemma":[0.9989001,0.0002583736,0.0001467719,0.0002689945,0.0003671145,0.00005862493],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0002601992,0.0001753931,0.001087086,0.0009553203,0.0001751776,0.0006208866,0.0001754662,0.02022687,0.03410247,0.203743,0.0247008,0.7137773],"study_design_scores_gemma":[0.0001867076,0.0006562445,0.001681417,0.0003817478,0.0001850099,0.004053798,0.0002121599,0.4354765,0.02527416,0.2019676,0.3297322,0.0001924546],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.009365622,0.007202867,0.9347108,0.000954117,0.0004675604,0.0003586293,0.0004601533,0.001471575,0.04500866],"genre_scores_gemma":[0.5715981,0.01231018,0.3515144,0.002151691,0.001313114,0.0008790517,0.001749984,0.0001186694,0.05836488],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01007231,"threshold_uncertainty_score":0.03369528,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.08173974885336378,"score_gpt":0.3325771200439259,"score_spread":0.2508373711905622,"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."}}