{"id":"W4400950565","doi":"10.3390/electronics13152909","title":"Compressive Sensing-Based Channel Estimation for Uplink and Downlink Reconfigurable Intelligent Surface-Aided Millimeter Wave Massive MIMO Systems","year":2024,"lang":"en","type":"article","venue":"Electronics","topic":"Advanced Wireless Communication Technologies","field":"Engineering","cited_by":10,"is_retracted":false,"has_abstract":true,"ca_institutions":"McMaster University","funders":"National Research Foundation","keywords":"Telecommunications link; Channel (broadcasting); MIMO; Computer science; Channel state information; Compressed sensing; Base station; Estimator; Electronic engineering; Overhead (engineering); Precoding; Wireless; Real-time computing; Computer network; Engineering; Telecommunications; Algorithm; Mathematics; 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":[0.0003684596,0.0004202129,0.000427314,0.0002161804,0.0001905823,0.0003665789,0.0003916953,0.0003531283,0.0003863961],"category_scores_gemma":[0.001339946,0.0001926991,0.0002521689,0.000301403,0.0003420516,0.0005893745,0.00047351,0.0004968347,0.0001299931],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002371337,"about_ca_system_score_gemma":0.0004299146,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001624127,"about_ca_topic_score_gemma":0.00188098,"domain_scores_codex":[0.999651,0.0001123962,0.00001331471,0.00004989755,0.0001302595,0.00004314148],"domain_scores_gemma":[0.9994562,0.0002874649,0.00009109927,0.00005916207,0.00008852308,0.00001746308],"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":[0.0001693474,0.0000483512,0.001018836,0.0001149826,0.00004516543,0.0001127452,0.00008949188,0.8381265,0.03118667,0.007943664,0.0007009543,0.1204433],"study_design_scores_gemma":[0.000003467483,0.00003696766,0.0001803006,0.000003814072,0.000004089381,0.00002042296,0.00001309245,0.9954643,0.003404672,0.0006597152,0.0002031506,0.000006035385],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.03977227,0.0003068898,0.9582417,0.0001027284,0.00002335083,0.00001902089,0.00003259916,0.0001597335,0.001341686],"genre_scores_gemma":[0.8957732,0.000399591,0.1026705,0.00007333956,0.00004408869,0.00004815165,0.0000717838,0.00001297527,0.0009064246],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.001624127,"threshold_uncertainty_score":0.00322932,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02162642329593608,"score_gpt":0.2469745176303409,"score_spread":0.2253480943344048,"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."}}