{"id":"W2114115739","doi":"10.1109/tcomm.2011.042111.100310","title":"Analysis and Design of Cooperative BICM-OFDM Systems","year":2011,"lang":"en","type":"article","venue":"IEEE Transactions on Communications","topic":"Cooperative Communication and Network Coding","field":"Computer Science","cited_by":18,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"","keywords":"Pairwise error probability; Orthogonal frequency-division multiplexing; Relay; Computer science; Disjoint sets; Cooperative diversity; Diversity gain; Multiplexing; Transmit diversity; Electronic engineering; Selection (genetic algorithm); Wireless; Diversity scheme; Computer network; Algorithm; Mathematics; Channel (broadcasting); Telecommunications; Wireless network; Engineering; Fading","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":[0.0008418394,0.0005810116,0.000647668,0.000435977,0.0003773443,0.0008392592,0.0007944483,0.000774699,0.001263162],"category_scores_gemma":[0.002839874,0.0004788426,0.0003056676,0.0004536612,0.0005265147,0.0006415286,0.0008573916,0.0004941833,0.0003020603],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008915841,"about_ca_system_score_gemma":0.0006758532,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001604248,"about_ca_topic_score_gemma":0.001599348,"domain_scores_codex":[0.9993659,0.0002702861,0.00001744136,0.00006182543,0.0002273248,0.000057372],"domain_scores_gemma":[0.9994466,0.0002664469,0.00008330746,0.00004019172,0.0001391738,0.00002438911],"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":[0.00004119844,0.00002158486,0.0004759795,0.00009610695,0.0000319104,0.0001299772,0.0001501717,0.9182475,0.004297252,0.05796715,0.0006882788,0.01785282],"study_design_scores_gemma":[0.000006282009,0.00002028538,0.00008489776,0.000008231325,0.000005948782,0.00003557616,0.00001885429,0.9905301,0.0004626602,0.008030431,0.0007907468,0.000006053229],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02953388,0.001108414,0.9597476,0.0002356332,0.00002839791,0.00005817172,0.0000428277,0.00007182701,0.00917325],"genre_scores_gemma":[0.8927886,0.001131079,0.1020609,0.0001026985,0.00004464438,0.0002304816,0.00007698844,0.00003115257,0.003533403],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.001604248,"threshold_uncertainty_score":0.006468892,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1283991337974955,"score_gpt":0.2979058112581192,"score_spread":0.1695066774606236,"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."}}