{"id":"W4399526704","doi":"10.1109/lwc.2024.3412974","title":"Mutual Information-Based Integrated Sensing and Communications: A WMMSE Framework","year":2024,"lang":"en","type":"article","venue":"IEEE Wireless Communications Letters","topic":"Distributed Sensor Networks and Detection Algorithms","field":"Computer Science","cited_by":19,"is_retracted":false,"has_abstract":true,"ca_institutions":"Concordia University","funders":"Natural Science Foundation of Sichuan Province; China Mobile Research Institute; Natural Science Foundation of Shenzhen City","keywords":"Computer science; Mutual information; Computer network; Telecommunications; Artificial intelligence","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":["scholarly_communication"],"consensus_categories":[],"category_scores_codex":[0.0003540313,0.0002112153,0.0001892049,0.0003344677,0.0006113372,0.001296731,0.002122467,0.0001257398,0.000004576701],"category_scores_gemma":[0.00006360879,0.0002121559,0.00008502784,0.001370317,0.0004196991,0.00109232,0.0004478058,0.0007721531,0.0001199808],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001159359,"about_ca_system_score_gemma":0.000112537,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001445701,"about_ca_topic_score_gemma":0.00003955327,"domain_scores_codex":[0.9984949,0.000286494,0.0004590878,0.0002529455,0.0002349827,0.0002715985],"domain_scores_gemma":[0.9948967,0.001151664,0.000109937,0.003558566,0.000161859,0.0001212885],"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":[0.000009647635,0.00008751047,0.00004414399,0.00006246054,0.0001242362,0.00001068462,0.002425694,0.002110596,0.001128227,0.07564817,0.01081933,0.9075293],"study_design_scores_gemma":[0.0001297024,0.00001491531,0.00003786482,0.0002264634,0.00001434699,0.00002865062,0.0001285008,0.891514,0.0001739998,0.000541957,0.1069604,0.0002291228],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.003653304,0.0008520997,0.9610167,0.03251658,0.0005119977,0.0002222635,0.00003740039,0.0008801651,0.0003094854],"genre_scores_gemma":[0.7304769,0.0003860926,0.2644834,0.004436149,0.00003863412,0.00003429255,0.0001161792,0.00001611752,0.00001229587],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9073002,"threshold_uncertainty_score":0.99974,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01770513243932297,"score_gpt":0.2586040502183978,"score_spread":0.2408989177790749,"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."}}