{"id":"W2115170659","doi":"10.1109/tsp.2010.2048208","title":"An Efficient Low-Complexity Detector for Spatially Multiplexed MC-CDM","year":2010,"lang":"en","type":"article","venue":"IEEE Transactions on Signal Processing","topic":"Advanced Wireless Communication Techniques","field":"Engineering","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"","keywords":"Multiplexing; Detector; Orthogonal frequency-division multiplexing; Computer science; Subcarrier; Single antenna interference cancellation; Bit error rate; Electronic engineering; Interference (communication); Signal-to-noise ratio (imaging); Algorithm; Diversity gain; Frequency-division multiplexing; Fading; Telecommunications; Engineering; Channel (broadcasting); Decoding methods","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":[0.0005453444,0.0004188346,0.0004471611,0.0004950362,0.0002940359,0.000482312,0.0007958463,0.0005897448,0.0009141171],"category_scores_gemma":[0.001069758,0.0002205641,0.000270181,0.0004372734,0.0004376073,0.0005542464,0.0004511295,0.0006432922,0.0004017523],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000408269,"about_ca_system_score_gemma":0.0006903767,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0003864959,"about_ca_topic_score_gemma":0.001000668,"domain_scores_codex":[0.9994096,0.0001153026,0.00001932885,0.00006949642,0.0003516982,0.00003467272],"domain_scores_gemma":[0.9995279,0.000181665,0.00006663118,0.00007273348,0.0001295833,0.00002139427],"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":[0.0003053718,0.0001613462,0.001985855,0.0002650812,0.0001105694,0.0002378873,0.0001192381,0.0762416,0.3892868,0.06772857,0.002982087,0.4605756],"study_design_scores_gemma":[0.0000282399,0.0001799311,0.0004140255,0.00001243227,0.00002664994,0.0004762349,0.00001120806,0.8656597,0.1224553,0.003496963,0.007199882,0.00003941379],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.007951162,0.0002690684,0.9907653,0.00008029248,0.00004445358,0.00002416159,0.00001713747,0.0001312382,0.0007171294],"genre_scores_gemma":[0.1702905,0.0002507003,0.8272971,0.000171767,0.00006773768,0.0000618734,0.000047548,0.00001518152,0.001797594],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.0009141171,"threshold_uncertainty_score":0.003058076,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02312014317086834,"score_gpt":0.2807089933306833,"score_spread":0.2575888501598149,"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."}}