{"id":"W4387491138","doi":"10.1109/tvt.2023.3323563","title":"Integrated Sensing and Communication in mmWave Wireless Backhaul Networks","year":2023,"lang":"en","type":"article","venue":"IEEE Transactions on Vehicular Technology","topic":"Millimeter-Wave Propagation and Modeling","field":"Engineering","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"Toronto Metropolitan University","funders":"National Natural Science Foundation of China","keywords":"Computer science; Wireless; Scheduling (production processes); Backhaul (telecommunications); Bandwidth (computing); Throughput; Wireless network; Computer network; Communications system; Real-time computing; Distributed computing; Electronic engineering; Engineering; 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":[0.0004347343,0.0005066567,0.000444457,0.0002422361,0.0003458993,0.0007737863,0.0005391607,0.0005349378,0.00105232],"category_scores_gemma":[0.0007044117,0.0001994231,0.0002017611,0.0006353747,0.000405276,0.0008233042,0.0006278753,0.0005059566,0.0002116887],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005572349,"about_ca_system_score_gemma":0.0005761549,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001428817,"about_ca_topic_score_gemma":0.002056283,"domain_scores_codex":[0.9995467,0.0001560658,0.00001399371,0.00006798605,0.0001293576,0.0000858989],"domain_scores_gemma":[0.999673,0.0001685944,0.00005144354,0.00004171351,0.00004813465,0.00001707667],"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":[0.000211998,0.0001054874,0.0007706931,0.0002155216,0.00003531178,0.0001864734,0.0001013958,0.7639365,0.03200481,0.04362789,0.002196235,0.1566077],"study_design_scores_gemma":[0.00000522778,0.00008269956,0.0002053293,0.000007013887,0.000006795681,0.0000500755,0.0000253819,0.9875017,0.00422808,0.006387292,0.001493848,0.000006580051],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.02642705,0.001313814,0.9674522,0.0002263493,0.00005603536,0.00003793434,0.00004051706,0.0002020474,0.004244111],"genre_scores_gemma":[0.8293027,0.001358852,0.1657026,0.0001809543,0.00008526672,0.000108703,0.00007175749,0.00002769293,0.003161593],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.001428817,"threshold_uncertainty_score":0.004042983,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.013424472747629,"score_gpt":0.2136564472929077,"score_spread":0.2002319745452787,"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."}}