{"id":"W4388426759","doi":"10.1109/spawc53906.2023.10304519","title":"Active Sensing for Reciprocal MIMO Channels","year":2023,"lang":"en","type":"article","venue":"","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Precoding; Decoding methods; MIMO; Computer science; Transmitter; Channel state information; Overhead (engineering); Channel (broadcasting); Duplex (building); Algorithm; Theoretical computer science; Computer engineering; Artificial intelligence; Wireless; Computer network; 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":[0.00004440502,0.00006714495,0.0000757475,0.0001020797,0.00004766111,0.00001550208,0.00005051439,0.00007762855,0.00001646815],"category_scores_gemma":[0.00005525249,0.0000618823,0.00003262354,0.0002670486,0.00001477094,0.00005239598,0.00001614271,0.00004607295,0.0001030818],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002362887,"about_ca_system_score_gemma":0.000003746,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000002394435,"about_ca_topic_score_gemma":0.00000384169,"domain_scores_codex":[0.999606,0.000002205723,0.00007692837,0.00008296921,0.00004698929,0.0001849081],"domain_scores_gemma":[0.999817,0.00004820517,0.00000572855,0.00008379347,0.00002869698,0.00001664033],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00004476638,0.0000129585,0.00005267152,0.0002736823,0.0001500003,0.00001877141,0.001887736,0.133487,0.02168236,0.02650973,0.1677867,0.6480936],"study_design_scores_gemma":[0.0002128354,0.00002059109,0.00004402254,0.00001143454,0.000004962808,0.000001998752,0.0008779635,0.53751,0.430843,0.004153521,0.02616363,0.0001560985],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.06937667,0.00002638647,0.9009834,0.0004655079,0.001407834,0.000435204,0.00001669684,0.01036345,0.01692487],"genre_scores_gemma":[0.9961264,0.00002089321,0.002427173,0.00005383025,0.0000910666,0.00001649739,0.00001998358,0.00002824155,0.001215949],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9267497,"threshold_uncertainty_score":0.2523487,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02121822925910617,"score_gpt":0.2437768978295839,"score_spread":0.2225586685704777,"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."}}