{"id":"W4403095623","doi":"10.1109/jstsp.2024.3474254","title":"Integrated Sensing and Communications for End-to-End Predictive Beamforming Design in Vehicle-to-Infrastructure Networks","year":2024,"lang":"en","type":"article","venue":"IEEE Journal of Selected Topics in Signal Processing","topic":"Millimeter-Wave Propagation and Modeling","field":"Engineering","cited_by":14,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"Alliance de recherche numérique du Canada; Deutsche Forschungsgemeinschaft","keywords":"Beamforming; End-to-end principle; Computer science; Computer network; Telecommunications","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007346649,0.0009690671,0.0005459108,0.0002735281,0.0003464821,0.0006763167,0.001164861,0.0007063952,0.002047598],"category_scores_gemma":[0.00134951,0.0003927306,0.0004178127,0.0004728037,0.000654497,0.001273491,0.0009039914,0.001360918,0.0005753661],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006572194,"about_ca_system_score_gemma":0.001226269,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003820685,"about_ca_topic_score_gemma":0.006373147,"domain_scores_codex":[0.9995005,0.0001085258,0.00002566831,0.00009835991,0.0001843269,0.00008268672],"domain_scores_gemma":[0.9995473,0.0001610592,0.00005125481,0.00003699266,0.0001758132,0.00002758153],"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":[0.00007812586,0.00004907149,0.0004993696,0.00006635643,0.0000229613,0.00007260634,0.00005755774,0.8685767,0.007222606,0.01448744,0.001854763,0.1070125],"study_design_scores_gemma":[0.00000390469,0.00003478706,0.00003988737,0.000005075275,0.000004913134,0.00001456491,0.000007279125,0.9950815,0.001250348,0.002953932,0.0005991556,0.000004673431],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.003643028,0.000168818,0.9940753,0.0001030299,0.00002682725,0.0000190989,0.00002320861,0.0001767572,0.001763843],"genre_scores_gemma":[0.7686382,0.000709289,0.225884,0.000364374,0.00008098222,0.0002096612,0.0001982946,0.0000491049,0.003866127],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.003820685,"threshold_uncertainty_score":0.00759691,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0234887424068782,"score_gpt":0.2625484489879265,"score_spread":0.2390597065810483,"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."}}