{"id":"W2740929474","doi":"10.1109/cwit.2017.7994835","title":"Efficient lattice-reduction-aided conditional detection for MIMO systems","year":2017,"lang":"en","type":"article","venue":"","topic":"Advanced Wireless Communication Techniques","field":"Engineering","cited_by":7,"is_retracted":false,"has_abstract":true,"ca_institutions":"Huawei Technologies (Canada); University of Waterloo","funders":"","keywords":"Lattice reduction; MIMO; Reduction (mathematics); Computer science; Algorithm; Diagonal; Quadrature amplitude modulation; Network packet; Channel (broadcasting); Performance metric; Detector; Metric (unit); Matrix (chemical analysis); Mathematics; Bit error rate; Decoding methods; Telecommunications","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":[0.0009451538,0.0007310835,0.0008487044,0.0003933794,0.0003876424,0.000907378,0.001051447,0.0006032852,0.001520617],"category_scores_gemma":[0.002534227,0.000339042,0.0004019078,0.0006654774,0.0007576409,0.001176684,0.001558078,0.001249702,0.0005095053],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004737374,"about_ca_system_score_gemma":0.001362942,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000682444,"about_ca_topic_score_gemma":0.001273732,"domain_scores_codex":[0.9987765,0.0004736084,0.00005033085,0.0001251607,0.0004413284,0.0001330132],"domain_scores_gemma":[0.9987336,0.0006471885,0.0001370617,0.0002145461,0.0001957184,0.00007195507],"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":[0.0007595646,0.0002289377,0.0009303034,0.0002564479,0.00009599721,0.0001924188,0.0002224599,0.4110442,0.09638515,0.1267834,0.005835353,0.3572658],"study_design_scores_gemma":[0.00002306402,0.00009756152,0.0001145563,0.000007264763,0.000006759,0.00009834621,0.00001056115,0.9781681,0.01253324,0.007852143,0.001059596,0.00002874396],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.01607823,0.0003027508,0.9815803,0.0001804534,0.00004370324,0.00002470003,0.00006894736,0.000348847,0.001372088],"genre_scores_gemma":[0.4816628,0.0002725809,0.515243,0.0001832239,0.00006331488,0.00009514466,0.0001880154,0.00004070197,0.002251182],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.001520617,"threshold_uncertainty_score":0.005086899,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02290503300326677,"score_gpt":0.28257543871812,"score_spread":0.2596704057148532,"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."}}