{"id":"W2582115116","doi":"10.1109/tvt.2017.2657696","title":"Structured-Compressed-Sensing-Based Impulsive Noise Cancelation for MIMO Systems","year":2017,"lang":"en","type":"article","venue":"IEEE Transactions on Vehicular Technology","topic":"Power Line Communications and Noise","field":"Engineering","cited_by":16,"is_retracted":false,"has_abstract":true,"ca_institutions":"Western University","funders":"National Natural Science Foundation of China","keywords":"MIMO; Compressed sensing; Robustness (evolution); Computer science; Matching pursuit; Greedy algorithm; Wireless; Precoding; Channel state information; Algorithm; Electronic engineering; Channel (broadcasting); Engineering; 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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00007427358,0.0002070838,0.0002464621,0.0003259807,0.0006302297,0.00007517092,0.0005017533,0.0003301568,0.000007480184],"category_scores_gemma":[0.00001412997,0.0002174852,0.0001152409,0.0001297022,0.0001372479,0.0001182335,0.000002298088,0.0003629246,0.00001430574],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001108116,"about_ca_system_score_gemma":0.00003421522,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00007375493,"about_ca_topic_score_gemma":0.0001885361,"domain_scores_codex":[0.9991388,0.00001774013,0.0002494939,0.0002285283,0.00009748617,0.0002679091],"domain_scores_gemma":[0.9981722,0.00005889548,0.00009860568,0.001479997,0.0001383669,0.00005195567],"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":[0.00003104864,0.00005104595,0.0000267311,0.00008213994,0.0001193528,0.000005667352,0.00002883426,0.929848,0.04132809,0.0004360517,0.000220867,0.02782214],"study_design_scores_gemma":[0.0009147142,0.00007974118,0.0001554926,0.00008493869,0.00007954216,0.00001388641,0.0000348748,0.8189248,0.1689066,0.0003730991,0.01013968,0.0002926333],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.09086774,0.0004519152,0.9054033,0.0005838392,0.001144002,0.0005813278,0.0001221743,0.0007002323,0.0001455068],"genre_scores_gemma":[0.9947317,0.00005775327,0.004876521,0.00002037406,0.0000377817,0.0001605568,0.00001327653,0.0000503967,0.00005164554],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.903864,"threshold_uncertainty_score":0.8868788,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01228190167016751,"score_gpt":0.2454525219107163,"score_spread":0.2331706202405488,"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."}}