{"id":"W2083580719","doi":"10.1109/intlec.2006.251659","title":"Improving HomePlug Power Line Communications with LDPC Coded OFDM","year":2006,"lang":"en","type":"article","venue":"","topic":"Power Line Communications and Noise","field":"Engineering","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"Queen's University","funders":"","keywords":"Orthogonal frequency-division multiplexing; Low-density parity-check code; Computer science; Fading; Power-line communication; Electronic engineering; Throughput; Multipath propagation; Decoding methods; Impulse noise; Coding (social sciences); Channel (broadcasting); Algorithm; Telecommunications; Power (physics); Wireless; Engineering; Mathematics; Artificial intelligence","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":[0.0003762801,0.0004917214,0.000388235,0.0003318185,0.0002552631,0.0003930716,0.0003873088,0.0004082766,0.0009334575],"category_scores_gemma":[0.001944748,0.0001162842,0.0001333867,0.0004718938,0.0003621004,0.0006914568,0.0005386593,0.0002696833,0.000291209],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003708551,"about_ca_system_score_gemma":0.0003481458,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001265356,"about_ca_topic_score_gemma":0.001735734,"domain_scores_codex":[0.9995691,0.0001347567,0.00001242781,0.00004092009,0.0001861095,0.00005663097],"domain_scores_gemma":[0.9993223,0.0003544476,0.0001054069,0.00008115781,0.0001148657,0.00002179453],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0007276241,0.000345919,0.006297238,0.0002223606,0.00006178926,0.000606273,0.0004166828,0.4317669,0.1892103,0.01744842,0.002824448,0.3500721],"study_design_scores_gemma":[0.00003139835,0.0001936232,0.0008745141,0.00001718051,0.00002217437,0.0002171888,0.00003048223,0.9230093,0.07177901,0.00152018,0.002284784,0.00002014506],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.44891,0.0006595823,0.5379934,0.0002300337,0.00002715602,0.00004492946,0.00006674269,0.001718585,0.01034955],"genre_scores_gemma":[0.9192247,0.0002951229,0.07900853,0.00004468972,0.00001737287,0.00001875679,0.00005826854,0.00003246017,0.001300048],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.001265356,"threshold_uncertainty_score":0.003122747,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.008891744377333878,"score_gpt":0.2101508767015161,"score_spread":0.2012591323241822,"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."}}