{"id":"W3010834347","doi":"10.1109/tcomm.2020.2981332","title":"Improper Gaussian Signaling for Integrated Data and Energy Networking","year":2020,"lang":"en","type":"article","venue":"IEEE Transactions on Communications","topic":"Energy Harvesting in Wireless Networks","field":"Engineering","cited_by":25,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Engineering and Physical Sciences Research Council; Institute for Computational Science and Technology; Queen's University; National Natural Science Foundation of China; Queen's University Belfast; Royal Society; Australian Research Council; Royal Academy of Engineering","keywords":"Beamforming; Computer science; Telecommunications link; Throughput; Base station; Gaussian; Relay; Energy (signal processing); Computer network; Electronic engineering; Telecommunications; Engineering; Wireless; Mathematics; Power (physics)","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001069015,0.0001609897,0.000157387,0.00005231667,0.0003497575,0.00007459563,0.0008822235,0.00009482427,0.00001122477],"category_scores_gemma":[0.000007744093,0.0001683959,0.00003735523,0.0002977431,0.00008308262,0.0001778563,0.0000125775,0.0003501392,0.000003199517],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000302516,"about_ca_system_score_gemma":0.00002256648,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0000396406,"about_ca_topic_score_gemma":0.0002271395,"domain_scores_codex":[0.9991959,0.0000562684,0.0002524152,0.000223862,0.00006964088,0.0002019313],"domain_scores_gemma":[0.9981803,0.0003757836,0.00003214841,0.001247344,0.00004247287,0.0001219299],"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":[0.00001660985,0.00004509562,0.000006313703,0.0000372856,0.0001492136,5.480907e-7,0.0003424747,0.8264122,0.00519376,0.0005659289,0.001402743,0.1658278],"study_design_scores_gemma":[0.0001955055,0.00002881326,0.000002363897,0.00007176503,0.0000487594,0.000002399674,0.00006764933,0.9233383,0.001668634,0.00003802837,0.07436251,0.0001752748],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.0004523002,0.0008894045,0.9957022,0.001017178,0.0002578007,0.00009225996,0.0001141679,0.0005505203,0.0009241546],"genre_scores_gemma":[0.9694114,0.001301067,0.02853394,0.0002528547,0.0001200838,0.0001092594,0.0001322152,0.00006775864,0.00007137188],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9689592,"threshold_uncertainty_score":0.6866987,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06699094452326423,"score_gpt":0.2590744376588732,"score_spread":0.1920834931356089,"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."}}