{"id":"W3090441074","doi":"10.1109/camad50429.2020.9209265","title":"Low Complexity PIC-MMSE Detector for LDS Systems over Frequency-Nonselective Channels","year":2020,"lang":"en","type":"article","venue":"","topic":"Advanced Wireless Communication Techniques","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Ottawa","funders":"","keywords":"Minimum mean square error; Single antenna interference cancellation; Rayleigh fading; Detector; Bit error rate; Interference (communication); Computer science; Channel (broadcasting); Fading; Algorithm; Electronic engineering; Telecommunications; Mathematics; Statistics; Engineering","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":[0.0006385971,0.0005709659,0.0006294516,0.0002785956,0.0003310564,0.0006475918,0.0005526543,0.000692284,0.0008252246],"category_scores_gemma":[0.001930371,0.0002606674,0.000269473,0.0003320185,0.000578332,0.0005756922,0.0004623131,0.0005885788,0.0002287679],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005168124,"about_ca_system_score_gemma":0.0007515262,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001482955,"about_ca_topic_score_gemma":0.002679577,"domain_scores_codex":[0.9994934,0.0001523147,0.00001728389,0.00005433341,0.0002255563,0.00005716466],"domain_scores_gemma":[0.9991497,0.0005236607,0.00008198519,0.00007293556,0.0001480802,0.00002369656],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0004883586,0.000115141,0.003985567,0.0002843438,0.0001230024,0.0005136462,0.0002107268,0.8091705,0.07004406,0.04198202,0.001094806,0.07198771],"study_design_scores_gemma":[0.000008440601,0.00007075949,0.0003252531,0.000005265029,0.00001613427,0.0001052505,0.00001112026,0.9863666,0.01081487,0.00182857,0.0004372048,0.00001052547],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1230316,0.0008732498,0.8699919,0.0002024501,0.0000411654,0.00004509207,0.00005457598,0.0003068662,0.00545314],"genre_scores_gemma":[0.8780925,0.0004366048,0.1188339,0.0000911137,0.00004812563,0.00002855349,0.00004079608,0.00001644254,0.002411964],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.001482955,"threshold_uncertainty_score":0.003749788,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0430783987707559,"score_gpt":0.2680421815082535,"score_spread":0.2249637827374976,"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."}}