{"id":"W2920160035","doi":"10.3390/s19051061","title":"Robust Distributed Collaborative Beamforming for Wireless Sensor Networks with Channel Estimation Impairments","year":2019,"lang":"en","type":"article","venue":"Sensors","topic":"Advanced MIMO Systems Optimization","field":"Engineering","cited_by":16,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto; Institut National de la Recherche Scientifique; Université du Québec à Montréal","funders":"Division of Administrative Services; Natural Sciences and Engineering Research Council of Canada","keywords":"Beamforming; Wireless sensor network; Channel (broadcasting); Computer science; Channel state information; Wireless; Node (physics); Ranging; Signal-to-noise ratio (imaging); Wireless network; Electronic engineering; Real-time computing; Computer network; Telecommunications; Engineering","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":[0.0008900876,0.001318825,0.000775683,0.0003849515,0.000360753,0.0005955543,0.001241297,0.0009215517,0.0009878046],"category_scores_gemma":[0.001884368,0.0003864497,0.0006312654,0.000732982,0.0006559094,0.001044781,0.001259811,0.0008907443,0.0005393274],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003970147,"about_ca_system_score_gemma":0.0006813763,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000967285,"about_ca_topic_score_gemma":0.001156394,"domain_scores_codex":[0.9991564,0.000243376,0.00003242644,0.0001647072,0.0003251294,0.00007804773],"domain_scores_gemma":[0.9992585,0.0003158149,0.0001253087,0.0001110515,0.0001591275,0.00003021261],"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":[0.0001361612,0.00006639866,0.000328941,0.0001944046,0.0001166022,0.00009021076,0.00009810382,0.7781307,0.03954362,0.02165662,0.001715854,0.1579224],"study_design_scores_gemma":[0.0000179609,0.0001251642,0.0001085501,0.00001165964,0.0000202497,0.00006207533,0.00001682057,0.9881901,0.005090168,0.00486815,0.001473759,0.00001535113],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.002164589,0.0001325567,0.9970639,0.00003842652,0.00001280102,0.000008366332,0.000008126842,0.00006817994,0.0005029702],"genre_scores_gemma":[0.4335902,0.0009231015,0.5611326,0.0002386259,0.0001448565,0.0002261851,0.0001349846,0.00008390962,0.003525527],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.001318825,"threshold_uncertainty_score":0.004707336,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.006596363708176575,"score_gpt":0.2011831345151606,"score_spread":0.194586770806984,"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."}}