{"id":"W3175772452","doi":"10.1109/lcomm.2021.3091997","title":"Performance Improvement of LoRa Modulation With Signal Combining and Semi-Coherent Detection","year":2021,"lang":"en","type":"article","venue":"IEEE Communications Letters","topic":"IoT Networks and Protocols","field":"Engineering","cited_by":21,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Saskatchewan","funders":"","keywords":"Computer science; Additive white Gaussian noise; Rayleigh fading; Overhead (engineering); Channel state information; Electronic engineering; Modulation (music); Algorithm; Detection theory; Fading; Channel (broadcasting); Telecommunications; Detector; Wireless; Physics; Engineering","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":[0.0018231,0.0009636534,0.0009625582,0.000508244,0.0003738327,0.001095912,0.000581362,0.0008654638,0.0006088426],"category_scores_gemma":[0.007635392,0.0002811795,0.0003565161,0.0008374287,0.001015346,0.00127952,0.001539921,0.0007632043,0.0003027231],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005734903,"about_ca_system_score_gemma":0.0004330121,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0003344099,"about_ca_topic_score_gemma":0.0003387898,"domain_scores_codex":[0.9972198,0.001333942,0.00008618258,0.0002964526,0.0006997696,0.0003639109],"domain_scores_gemma":[0.9956043,0.002713517,0.0005710219,0.0004702755,0.0005493586,0.00009160431],"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":[0.001208893,0.0003861998,0.0103104,0.0003793876,0.0002889498,0.0006739486,0.0007026403,0.5912787,0.1314947,0.0446023,0.001282239,0.2173916],"study_design_scores_gemma":[0.00003416418,0.0006705804,0.001542836,0.00002315673,0.00006050271,0.0005695964,0.00006439104,0.9581547,0.03119626,0.006450376,0.001189283,0.00004426928],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.4402527,0.001839576,0.5429758,0.0005097429,0.00006582304,0.00006980767,0.00005837374,0.0007609194,0.01346725],"genre_scores_gemma":[0.9610316,0.0002597359,0.03807089,0.00008092178,0.00004447166,0.00002167092,0.00002223406,0.00001975419,0.0004487745],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.0018231,"threshold_uncertainty_score":0.009641588,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01530963068123922,"score_gpt":0.2187001532102205,"score_spread":0.2033905225289813,"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."}}