{"id":"W4385756464","doi":"10.1109/twc.2023.3302319","title":"Simultaneous Beam Training and Target Sensing in ISAC Systems With RIS","year":2023,"lang":"en","type":"article","venue":"IEEE Transactions on Wireless Communications","topic":"Advanced Wireless Communication Technologies","field":"Engineering","cited_by":42,"is_retracted":false,"has_abstract":true,"ca_institutions":"Memorial University of Newfoundland","funders":"Natural Sciences and Engineering Research Council of Canada; National Natural Science Foundation of China","keywords":"Computer science; Scheme (mathematics); Beam (structure); Line-of-sight; Domain (mathematical analysis); Base station; Non-line-of-sight propagation; Real-time computing; Doppler effect; Simulation; Electronic engineering; Wireless; Telecommunications; Optics; Aerospace engineering; Physics; Engineering; Mathematics","routes":{"ca_aff":true,"ca_fund":true,"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":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0001730799,0.0002504469,0.0003228807,0.0005798576,0.0004088963,0.0000632116,0.0006494866,0.0001561857,0.00000427361],"category_scores_gemma":[0.0000146953,0.0002668772,0.00003826631,0.001325099,0.0003122849,0.0002217287,0.00001369196,0.000762235,0.00003748492],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001399288,"about_ca_system_score_gemma":0.00002842975,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00005002099,"about_ca_topic_score_gemma":0.0003607823,"domain_scores_codex":[0.9987692,0.0000869279,0.000387899,0.0002311727,0.0001669326,0.0003579181],"domain_scores_gemma":[0.9968761,0.001103348,0.00006128514,0.001824661,0.00006382277,0.00007079595],"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":[0.000006527338,0.00003820869,0.00001904033,0.00003049334,0.00004179485,0.000006544052,0.001333775,0.9418864,0.001575041,0.0002166043,0.00001110261,0.05483447],"study_design_scores_gemma":[0.0004175903,0.00004519007,0.00005887784,0.000242117,0.00001430392,0.00003046717,0.004736162,0.9895998,0.003191325,0.0002048008,0.001100475,0.0003588755],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1679575,0.0009520407,0.8252624,0.0006937842,0.0001764683,0.0006335428,0.00008314085,0.00330164,0.000939463],"genre_scores_gemma":[0.985858,0.004514188,0.009286777,0.00001441871,0.000005964679,0.0001446373,0.00002122016,0.000080887,0.00007390801],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.8179005,"threshold_uncertainty_score":0.9999784,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0268085898474024,"score_gpt":0.2525566853015169,"score_spread":0.2257480954541145,"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."}}