{"id":"W2963539768","doi":"10.1109/twc.2019.2924220","title":"Deep Learning-Based Sphere Decoding","year":2019,"lang":"en","type":"article","venue":"IEEE Transactions on Wireless Communications","topic":"Advanced Wireless Communication Techniques","field":"Engineering","cited_by":86,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"","keywords":"Hypersphere; Decoding methods; Algorithm; Computer science; Sequential decoding; List decoding; Deep learning; Artificial neural network; Computational complexity theory; Range (aeronautics); MIMO; Mathematics; Artificial intelligence; Telecommunications; Concatenated error correction code; Block code","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":[0.0009507544,0.001100003,0.001270843,0.0006749926,0.0005662506,0.001407649,0.001398193,0.001222672,0.003033643],"category_scores_gemma":[0.004436823,0.0003880389,0.0006130519,0.001108621,0.001008118,0.001836342,0.001902561,0.001518928,0.00211679],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001241808,"about_ca_system_score_gemma":0.002108027,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.006952116,"about_ca_topic_score_gemma":0.006292436,"domain_scores_codex":[0.9991012,0.0002525505,0.00006763382,0.0001536751,0.0003188758,0.0001061852],"domain_scores_gemma":[0.998579,0.0005169329,0.00008510026,0.0002408836,0.0004903889,0.00008761251],"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":[0.0002375907,0.00006377621,0.000891951,0.0001144842,0.00005684884,0.0001463102,0.000124483,0.570214,0.009355362,0.05021847,0.008206172,0.3603705],"study_design_scores_gemma":[0.000005161573,0.0000122527,0.00002956937,0.000004698057,0.000002285998,0.00003308229,0.000006229223,0.9898311,0.003092296,0.005963001,0.001013692,0.000006594824],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.004204544,0.0002248543,0.9923834,0.0001105582,0.00004451336,0.00001878634,0.0000640175,0.0007747603,0.002174565],"genre_scores_gemma":[0.2896509,0.0006400942,0.6985355,0.0003975986,0.0001030787,0.00012292,0.0008498767,0.0004493546,0.009250641],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.006952116,"threshold_uncertainty_score":0.01382327,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01617498773448978,"score_gpt":0.2573277057188038,"score_spread":0.241152717984314,"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."}}