{"id":"W4214828252","doi":"10.1109/jsac.2022.3155496","title":"Active Sensing for Communications by Learning","year":2022,"lang":"en","type":"article","venue":"IEEE Journal on Selected Areas in Communications","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":62,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"Science and Engineering Research Council; Huawei Technologies","keywords":"Computer science; Channel state information; Frame (networking); Wireless; Channel (broadcasting); Deep learning; Beamforming; Artificial intelligence; Exploit; Machine learning; 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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001133189,0.0009384852,0.0007480041,0.0003983931,0.0003851369,0.001278755,0.001311136,0.001566044,0.002912372],"category_scores_gemma":[0.00341352,0.0004089047,0.0005273336,0.0005752213,0.001742366,0.00270595,0.001544885,0.00232022,0.0004612663],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009213757,"about_ca_system_score_gemma":0.0007573709,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002263949,"about_ca_topic_score_gemma":0.002008783,"domain_scores_codex":[0.9993917,0.0001864968,0.00003048047,0.0001408621,0.0001854499,0.00006493496],"domain_scores_gemma":[0.9986846,0.0008955129,0.00009872,0.0001391218,0.0001450707,0.00003690325],"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":[0.000144374,0.00006402418,0.0005099327,0.0002893771,0.00008128494,0.0001043477,0.000154091,0.601758,0.00649634,0.2487465,0.004971193,0.1366805],"study_design_scores_gemma":[0.000009334372,0.00002774835,0.00005394585,0.00001911278,0.000006661312,0.0000214766,0.00001058752,0.9364088,0.0009652682,0.05945271,0.003015303,0.000009154885],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.003440311,0.001276129,0.990567,0.0006532717,0.0001250844,0.0000181535,0.00003121401,0.0001813723,0.003707429],"genre_scores_gemma":[0.737065,0.004370167,0.2426177,0.001178068,0.000677378,0.0002754262,0.0002019778,0.0001450078,0.01346925],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.002912372,"threshold_uncertainty_score":0.009742856,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02444263985850341,"score_gpt":0.2750246822912213,"score_spread":0.2505820424327179,"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."}}