{"id":"W2996294337","doi":"10.1109/wcsp.2019.8928030","title":"Deep Learning for Compressed Sensing Based Channel Estimation in Millimeter Wave Massive MIMO","year":2019,"lang":"en","type":"article","venue":"","topic":"Millimeter-Wave Propagation and Modeling","field":"Engineering","cited_by":14,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"","keywords":"Matching pursuit; Channel (broadcasting); Compressed sensing; Computer science; MIMO; Artificial neural network; Algorithm; Extremely high frequency; Artificial intelligence; Electronic engineering; Telecommunications; Engineering","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":[0.0004377922,0.0006127493,0.0005463455,0.0002383123,0.0002744421,0.000428366,0.0006251896,0.0005632545,0.0008026299],"category_scores_gemma":[0.001404614,0.0002122329,0.000287258,0.0004725753,0.0005486567,0.0007781642,0.0007298882,0.000890796,0.0001328558],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004568225,"about_ca_system_score_gemma":0.0007576589,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005853073,"about_ca_topic_score_gemma":0.007125533,"domain_scores_codex":[0.9997194,0.00006727998,0.00001073053,0.00004902894,0.0001025691,0.00005095272],"domain_scores_gemma":[0.9994961,0.000278348,0.00005717186,0.00004647653,0.00009861901,0.00002323422],"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":[0.0001922458,0.00007662107,0.001207839,0.0001404817,0.00005451034,0.000181326,0.00006821256,0.8573564,0.01051305,0.01488577,0.001906782,0.1134168],"study_design_scores_gemma":[0.000002235831,0.0000148898,0.00007052813,0.000002432226,0.000002817517,0.00001287474,0.000004971954,0.9979926,0.0007794772,0.0009917528,0.0001224672,0.000002979699],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.03428385,0.0005704308,0.961924,0.0003734911,0.00006988054,0.00002912327,0.00009553359,0.0002676211,0.002386169],"genre_scores_gemma":[0.9040459,0.0006692652,0.09243618,0.0002174226,0.00008640075,0.00005624762,0.0001779762,0.00002502862,0.002285504],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.005853073,"threshold_uncertainty_score":0.01163805,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02159215197886299,"score_gpt":0.2232645108405376,"score_spread":0.2016723588616746,"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."}}