{"id":"W3189608635","doi":"10.1109/icc42927.2021.9500957","title":"A Tensor based Precoder and Receiver for MIMO GFDM systems","year":2021,"lang":"en","type":"article","venue":"","topic":"Tensor decomposition and applications","field":"Mathematics","cited_by":10,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"MIMO; Precoding; Tensor (intrinsic definition); Channel (broadcasting); Orthogonal frequency-division multiplexing; Computer science; MIMO-OFDM; Control theory (sociology); Interference (communication); Frequency domain; Topology (electrical circuits); Mathematics; Electronic engineering; Algorithm; Telecommunications; Engineering; Mathematical analysis; Geometry","routes":{"ca_aff":true,"ca_fund":true,"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":[0.0004618792,0.0007057139,0.0004715215,0.0002290295,0.0004391828,0.0006528735,0.0004990404,0.0007371391,0.001919018],"category_scores_gemma":[0.0008241333,0.0002010695,0.0004743195,0.0004858289,0.0005445632,0.0008578214,0.0004403646,0.000942594,0.0009387272],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006199356,"about_ca_system_score_gemma":0.0007566304,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002150541,"about_ca_topic_score_gemma":0.003270892,"domain_scores_codex":[0.9996228,0.0001160748,0.00001758931,0.00005342273,0.0001540487,0.00003599056],"domain_scores_gemma":[0.9997166,0.0000776877,0.00003287268,0.00005726043,0.00009970903,0.00001585351],"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":[0.0002840245,0.0001006586,0.0008134538,0.0002574597,0.0001105046,0.0005232721,0.0003122435,0.4771437,0.09556466,0.2274193,0.007588017,0.1898828],"study_design_scores_gemma":[0.000007271029,0.000112604,0.0001543293,0.00001420233,0.00001888198,0.0002284721,0.00001748846,0.9671205,0.01418709,0.01162986,0.006483132,0.00002611542],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.005383542,0.0002162746,0.9915241,0.0001670822,0.00007866102,0.00002156457,0.00006351771,0.000232443,0.002312884],"genre_scores_gemma":[0.3077318,0.0009928235,0.6781057,0.0002437501,0.0002363079,0.00007276898,0.0002583947,0.00008275277,0.01227564],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002150541,"threshold_uncertainty_score":0.006419778,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0691470913764628,"score_gpt":0.3325845956879464,"score_spread":0.2634375043114836,"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."}}