{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008699512,0.0008446577,0.0005549492,0.0003549823,0.0003618744,0.0009090647,0.000814471,0.0008101144,0.001476924],"category_scores_gemma":[0.002376346,0.0003592741,0.0005494247,0.0003457023,0.001075683,0.001197058,0.0009336873,0.0009665047,0.0003903485],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005195925,"about_ca_system_score_gemma":0.000617929,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001391061,"about_ca_topic_score_gemma":0.001698686,"domain_scores_codex":[0.9993069,0.0002222813,0.00002470435,0.000149514,0.000223233,0.00007336809],"domain_scores_gemma":[0.9989558,0.0006462657,0.0001014803,0.0001104207,0.0001541341,0.00003191429],"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":[0.0003097203,0.00009751305,0.0004668848,0.0002453875,0.00008361201,0.0002124519,0.0002797188,0.7004766,0.03414717,0.1002826,0.001471311,0.161927],"study_design_scores_gemma":[0.000009071605,0.00005184759,0.0000787756,0.000008042731,0.000009840955,0.00005259551,0.00001349799,0.9860203,0.003505795,0.009292902,0.0009450896,0.00001227589],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01086959,0.0002945146,0.9861591,0.0000890866,0.00003849224,0.00002115593,0.00002316448,0.0001356667,0.002369224],"genre_scores_gemma":[0.8025112,0.0008183013,0.1903715,0.0002071728,0.0001378458,0.0001013836,0.0001013135,0.0000475462,0.005703828],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.001476924,"threshold_uncertainty_score":0.004940748,"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."}}