{"id":"W3085215956","doi":"10.1109/tvt.2020.3025371","title":"Improper Gaussian Signaling for Computationally Tractable Energy and Information Beamforming","year":2020,"lang":"en","type":"article","venue":"IEEE Transactions on Vehicular Technology","topic":"Energy Harvesting in Wireless Networks","field":"Engineering","cited_by":11,"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","keywords":"Beamforming; Energy (signal processing); Decoding methods; Gaussian; Throughput; Computer science; Information transfer; Wireless; Transmitter power output; Electronic engineering; Power (physics); Energy harvesting; Maximum power transfer theorem; Wireless power transfer; Algorithm; Engineering; Telecommunications; Mathematics; Channel (broadcasting); Transmitter; 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":[0.00005485619,0.0001619864,0.0001721362,0.0002075351,0.0001561022,0.00003954504,0.0001125529,0.000253954,0.000007557775],"category_scores_gemma":[0.000007611803,0.0001721661,0.00004950901,0.000326386,0.00005178715,0.0004387008,0.000001362491,0.0002317185,0.00000437758],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003964341,"about_ca_system_score_gemma":0.00001737773,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000004828573,"about_ca_topic_score_gemma":0.000006917476,"domain_scores_codex":[0.9992439,0.000008039536,0.0002617308,0.0001535949,0.00009838934,0.000234363],"domain_scores_gemma":[0.9996633,0.00006167433,0.00003986442,0.0001053173,0.00005897781,0.00007087534],"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":[0.00001036919,0.000009185328,0.000002295762,0.00005266848,0.00004794856,0.000001415503,0.00006941288,0.9283813,0.01164819,0.001675177,0.00003613966,0.05806592],"study_design_scores_gemma":[0.0004411111,0.0001155846,0.000004061329,0.00003982004,0.0000299877,0.00001933204,0.00006287912,0.8935039,0.09322794,0.0002392625,0.01210834,0.0002078066],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.0424736,0.00009356777,0.9554244,0.0007081269,0.0001546874,0.0001322689,0.00001507223,0.000882832,0.0001154122],"genre_scores_gemma":[0.9760636,0.00005101297,0.02332713,0.0003326527,0.00004317335,0.000121735,0.00001604603,0.00003460271,0.00001004327],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.93359,"threshold_uncertainty_score":0.702073,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.007057688994181543,"score_gpt":0.1827414712420006,"score_spread":0.1756837822478191,"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."}}