{"id":"W2899461991","doi":"10.22215/etd/2018-12717","title":"SNR-Adaptive Constellation Design for Convolutional Codes","year":2018,"lang":"en","type":"dissertation","venue":"","topic":"Advanced Wireless Communication Techniques","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Carleton University","funders":"","keywords":"Convolutional code; Computer science; Algorithm; Decoding methods; Coding (social sciences); Encoder; Turbo code; Serial concatenated convolutional codes; Forward error correction; Computer engineering; Channel (broadcasting); Channel state information; Theoretical computer science; Electronic engineering; Concatenated error correction code; Telecommunications; Wireless; Block code; Mathematics; 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":[0.0005610922,0.0005290858,0.0002979397,0.0004578954,0.0002037218,0.0004647497,0.0004676663,0.0005548524,0.002416572],"category_scores_gemma":[0.003743987,0.0002684519,0.000248568,0.0005456213,0.000410825,0.0006541206,0.0008537719,0.0008431387,0.0008731272],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004970478,"about_ca_system_score_gemma":0.0007161468,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0006773924,"about_ca_topic_score_gemma":0.0009181753,"domain_scores_codex":[0.9994237,0.000191536,0.00002525223,0.00006941579,0.0002156232,0.00007448378],"domain_scores_gemma":[0.9990865,0.000414414,0.00008599005,0.0001149462,0.0002553099,0.00004275715],"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":[0.0005931481,0.00006586331,0.001589257,0.0002169112,0.00007021745,0.0002704121,0.0002488606,0.4373444,0.07223023,0.09953703,0.005030319,0.3828034],"study_design_scores_gemma":[0.00005092709,0.0002350187,0.000643694,0.00006743701,0.0000286622,0.0004936129,0.00003801407,0.9065154,0.02774077,0.05624634,0.007904034,0.00003604768],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.02767411,0.0005898776,0.962409,0.0003771125,0.00007117178,0.00002903794,0.0001030558,0.000323241,0.008423466],"genre_scores_gemma":[0.7263878,0.001282279,0.2632624,0.00021182,0.0001082408,0.00009084921,0.0003008061,0.0001264901,0.00822941],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.002416572,"threshold_uncertainty_score":0.008084297,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03654457928800842,"score_gpt":0.2904998147893752,"score_spread":0.2539552355013668,"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."}}