{"id":"W4390357311","doi":"10.1109/tvt.2023.3347926","title":"Channel Estimation for Backscatter Communication Systems Under Circuit Sensitivity Constraint","year":2023,"lang":"en","type":"article","venue":"IEEE Transactions on Vehicular Technology","topic":"Energy Harvesting in Wireless Networks","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta; Carleton University","funders":"Fundamental Research Funds for the Central Universities; National Natural Science Foundation of China","keywords":"Maximum a posteriori estimation; Estimator; Sensitivity (control systems); Channel (broadcasting); Backscatter (email); Algorithm; Constraint (computer-aided design); Minimum mean square error; Computer science; Signal-to-noise ratio (imaging); Mean squared error; Electronic engineering; Mathematics; Maximum likelihood; Statistics; Engineering; Telecommunications; Wireless","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":[0.00104335,0.0007544574,0.0008382965,0.0004590083,0.0003792873,0.00095236,0.0005240434,0.0008193795,0.001125688],"category_scores_gemma":[0.006633291,0.0004054178,0.0004235836,0.0006124962,0.0008534217,0.00147422,0.0009401902,0.001103885,0.0003449711],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006033171,"about_ca_system_score_gemma":0.001282959,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002890875,"about_ca_topic_score_gemma":0.002669523,"domain_scores_codex":[0.9991167,0.0002936931,0.00003654761,0.0001556698,0.0003025654,0.00009479006],"domain_scores_gemma":[0.9968812,0.002211022,0.000250889,0.0001750571,0.0004382812,0.00004361239],"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":[0.000225756,0.00005549664,0.002666872,0.000414259,0.0001164772,0.0001894859,0.0002177974,0.8183153,0.02402491,0.03622746,0.001701281,0.1158449],"study_design_scores_gemma":[0.00001074553,0.00003804329,0.0004893695,0.00002079275,0.00001954099,0.0001071283,0.0000232739,0.9880222,0.005076704,0.005507718,0.0006647359,0.00001967134],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01167879,0.0004984582,0.9865451,0.0001362135,0.00001893507,0.00001507943,0.00003483493,0.0001309493,0.0009416443],"genre_scores_gemma":[0.8110768,0.001985071,0.1832418,0.0002561516,0.0001139755,0.0001297822,0.0002421238,0.00006750797,0.002886812],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002890875,"threshold_uncertainty_score":0.005748093,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01988484202624348,"score_gpt":0.2294013438849304,"score_spread":0.2095165018586869,"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."}}