{"id":"W2510935666","doi":"10.1109/tcomm.2016.2603981","title":"Decoding Delay and Outage Performance Analysis of Full-Duplex Decode-Forward Relaying: Backward or Sliding Window Decoding","year":2016,"lang":"en","type":"article","venue":"IEEE Transactions on Communications","topic":"Full-Duplex Wireless Communications","field":"Engineering","cited_by":9,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Decoding methods; Sliding window protocol; Computer science; Sequential decoding; Relay; Fading; List decoding; Channel state information; Algorithm; Context (archaeology); Channel (broadcasting); Real-time computing; Telecommunications; Computer network; Electronic engineering; Block code; Wireless; Engineering; Window (computing); Concatenated error correction code","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0004632095,0.0003719165,0.0005982413,0.001123831,0.0008485537,0.00007109421,0.001386277,0.0002009714,0.0002090544],"category_scores_gemma":[0.00005695077,0.0003186314,0.0002887923,0.00183969,0.0002615223,0.0006237032,0.00003782212,0.0005128333,0.00004684008],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002868503,"about_ca_system_score_gemma":0.00006461766,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00004269019,"about_ca_topic_score_gemma":0.001440039,"domain_scores_codex":[0.997824,0.000169972,0.0009286707,0.000349665,0.0002768264,0.00045083],"domain_scores_gemma":[0.9943042,0.001993194,0.0001958629,0.003118654,0.0001851616,0.000202935],"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":[0.00009693162,0.0002535826,0.002134414,0.0001059543,0.002269637,0.000001584496,0.00188195,0.8508025,0.06485914,0.0006891151,0.00007143597,0.07683374],"study_design_scores_gemma":[0.0007653033,0.00008957791,0.002373415,0.0002986286,0.001081121,0.00002598764,0.0004053133,0.981913,0.01154834,0.000005715465,0.0009683537,0.0005252821],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6156152,0.000322152,0.3822817,0.0004912351,0.0001322528,0.0002594113,0.0001457339,0.0003647736,0.0003875619],"genre_scores_gemma":[0.958985,0.01020728,0.0302284,0.00003177457,0.00001203815,0.0001954648,0.00001943751,0.00008260772,0.0002380475],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3520533,"threshold_uncertainty_score":0.9999266,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04001104619709668,"score_gpt":0.2658316134852458,"score_spread":0.2258205672881491,"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."}}