{"id":"W2161976586","doi":"10.1109/tcsii.2013.2268433","title":"Pilot Allocation for Sparse Channel Estimation in MIMO-OFDM Systems","year":2013,"lang":"en","type":"article","venue":"IEEE Transactions on Circuits & Systems II Express Briefs","topic":"Sparse and Compressive Sensing Techniques","field":"Engineering","cited_by":72,"is_retracted":false,"has_abstract":true,"ca_institutions":"Concordia University","funders":"","keywords":"Orthogonal frequency-division multiplexing; Mutual coherence; MIMO; MIMO-OFDM; Channel (broadcasting); Computer science; Algorithm; Bit error rate; Coherence time; Coherence (philosophical gambling strategy); Compressed sensing; Mean squared error; Electronic engineering; Mathematics; Telecommunications; Engineering; Statistics","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":[0.0004334274,0.0002736799,0.0002809121,0.0001929709,0.0001741973,0.0002471739,0.0002742391,0.0003486965,0.0004433719],"category_scores_gemma":[0.002313345,0.0001572157,0.0001309039,0.0002994441,0.0004056965,0.000437276,0.0003820682,0.0003054686,0.0001238252],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001463637,"about_ca_system_score_gemma":0.0004047183,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0005629615,"about_ca_topic_score_gemma":0.0007611978,"domain_scores_codex":[0.9997272,0.0001329703,0.00001149805,0.00003034306,0.0000757423,0.00002224205],"domain_scores_gemma":[0.9992707,0.0004926012,0.00007181788,0.0000728038,0.00007504517,0.00001693431],"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":[0.0003745034,0.0000840576,0.001055244,0.0001709379,0.00004277129,0.0002243228,0.0001846156,0.6749153,0.04767396,0.02930919,0.001230134,0.244735],"study_design_scores_gemma":[0.00001698101,0.00007242036,0.0001693577,0.000008571403,0.000007169041,0.00005224522,0.00001225396,0.9904697,0.004741605,0.003998868,0.000445065,0.000005683479],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02861802,0.0002327958,0.9703043,0.00009225267,0.00001954999,0.00001702322,0.00001385447,0.00008867632,0.0006134629],"genre_scores_gemma":[0.7747277,0.0003809571,0.2241324,0.00005923343,0.00004898659,0.00007187259,0.00004219736,0.000013915,0.0005227748],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.0005629615,"threshold_uncertainty_score":0.002292216,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03140630475192765,"score_gpt":0.2351797863307149,"score_spread":0.2037734815787873,"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."}}