{"id":"W4408145145","doi":"10.1109/tccn.2025.3547726","title":"Deep Learning-Based Transceiver Design for Additive Non-Gaussian Impulsive Noise Channels","year":2025,"lang":"en","type":"article","venue":"IEEE Transactions on Cognitive Communications and Networking","topic":"Power Line Communications and Noise","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University","funders":"National Natural Science Foundation of China","keywords":"Computer science; Transceiver; Gaussian noise; Additive white Gaussian noise; Noise (video); Gaussian; Electronic engineering; Telecommunications; Artificial intelligence; Channel (broadcasting); Wireless; Engineering; Physics","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":[0.0008056263,0.0008431158,0.0006051957,0.0002486543,0.0002513262,0.0006635791,0.001258179,0.0007771653,0.001610013],"category_scores_gemma":[0.001417841,0.0004211381,0.0004187085,0.000295181,0.0006564958,0.0008306322,0.0009851599,0.001149797,0.0004227462],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006832035,"about_ca_system_score_gemma":0.0009922808,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001261933,"about_ca_topic_score_gemma":0.001761514,"domain_scores_codex":[0.9996275,0.00009547812,0.00001482309,0.00007326749,0.0001274835,0.00006144821],"domain_scores_gemma":[0.9995186,0.000179373,0.00006976884,0.00003927681,0.0001599035,0.00003300711],"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":[0.00004558856,0.00005072547,0.0004642722,0.00008761961,0.00004315444,0.0000791559,0.00006193401,0.9295785,0.006710476,0.01665543,0.00100559,0.04521766],"study_design_scores_gemma":[0.000002461262,0.00002600897,0.00002439291,0.000003232424,0.000006112469,0.00001725288,0.000003496138,0.9971513,0.0008561435,0.001626749,0.0002798783,0.000002939638],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.00490462,0.00007545413,0.9931512,0.00006759963,0.00001621265,0.00001493295,0.00001582072,0.0001300212,0.001624],"genre_scores_gemma":[0.7883104,0.0003752089,0.2038602,0.0003180653,0.00006531047,0.0001647743,0.000122013,0.0001029224,0.006681032],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.001610013,"threshold_uncertainty_score":0.005385995,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02466352514688571,"score_gpt":0.2709008771534028,"score_spread":0.2462373520065171,"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."}}