{"id":"W2799779867","doi":"10.1109/isit.2018.8437918","title":"Non-Asymptotic Achievable Rates for Gaussian Energy-Harvesting Channels: Best-Effort and Save-and-Transmit","year":2018,"lang":"en","type":"preprint","venue":"","topic":"Wireless Communication Security Techniques","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"Division of Electrical, Communications and Cyber Systems; National Science Foundation","keywords":"Independent and identically distributed random variables; Energy (signal processing); Transmitter power output; Mathematics; Dirty paper coding; Additive white Gaussian noise; Block (permutation group theory); Channel (broadcasting); Gaussian process; Gaussian; Markov process; Discrete mathematics; Combinatorics; Statistics; Transmitter; Random variable; Computer science; Telecommunications; White noise; Physics; Precoding; MIMO","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":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0003735074,0.0004691107,0.0005463557,0.0002255895,0.0001655929,0.0002864481,0.000513402,0.0004711098,0.0000294633],"category_scores_gemma":[0.00002698926,0.0004819611,0.00009852892,0.00009550682,0.0001369435,0.0001873645,0.0005424022,0.0004411046,0.000004716037],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00006064946,"about_ca_system_score_gemma":0.00003563163,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0004668332,"about_ca_topic_score_gemma":0.0001837559,"domain_scores_codex":[0.9984737,0.00002418448,0.0004822369,0.0004757921,0.0001414826,0.0004025759],"domain_scores_gemma":[0.9986337,0.0001635284,0.00009579898,0.000840373,0.0001096732,0.0001568975],"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":[0.0004586206,0.0017898,0.02855565,0.06852941,0.008665963,0.00005340482,0.02927007,0.04882529,0.05145994,0.2985862,0.05451918,0.4092865],"study_design_scores_gemma":[0.000902269,0.0002729566,0.000840925,0.002399109,0.0002903844,0.00003941387,0.0002066754,0.8385663,0.08650299,0.02706736,0.0406358,0.002275835],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2677732,0.007334752,0.6648287,0.00112909,0.001023514,0.003267545,0.0001437418,0.004921911,0.04957747],"genre_scores_gemma":[0.9354892,0.002817648,0.05968669,0.00008131181,0.0002165333,0.0006849982,0.0001215888,0.0001466856,0.0007553701],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.789741,"threshold_uncertainty_score":0.9997632,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02424268090729838,"score_gpt":0.2804427690806926,"score_spread":0.2562000881733942,"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."}}