{"id":"W2172011588","doi":"10.1109/vtcf.2006.105","title":"Antenna Selection for Space-Time Trellis Codes Over Block Rayleigh Fading Channels","year":2006,"lang":"en","type":"article","venue":"IEEE Vehicular Technology Conference","topic":"Advanced Wireless Communication Techniques","field":"Engineering","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"Concordia University","funders":"","keywords":"Fading; Pairwise error probability; Rayleigh fading; Antenna (radio); Computer science; Algorithm; Antenna diversity; Trellis (graph); Block code; Selection (genetic algorithm); Diversity gain; Telecommunications; Electronic engineering; Mathematics; Channel (broadcasting); Decoding methods; Engineering; 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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0001113802,0.0002862875,0.0003571353,0.0004757019,0.0001573087,0.00004241523,0.000479552,0.0004711295,0.00001756642],"category_scores_gemma":[0.00003363668,0.0003169761,0.00008476657,0.0005873329,0.0001732896,0.0001773219,0.00004578869,0.000370611,0.00002985126],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001231775,"about_ca_system_score_gemma":0.0000223708,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002756784,"about_ca_topic_score_gemma":0.00002925909,"domain_scores_codex":[0.9987859,0.00002301577,0.0003187699,0.0003264618,0.0001176051,0.0004282236],"domain_scores_gemma":[0.9990538,0.00007658896,0.00009749931,0.0005369073,0.0001999843,0.00003527246],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.000007708508,0.00003819366,0.0004276242,0.000042075,0.00004463528,0.000002743565,0.000023849,0.01556295,0.9693263,0.01116931,0.0008123229,0.00254235],"study_design_scores_gemma":[0.000272819,0.00005403221,0.0000394266,0.00007296577,0.00001977505,0.00001986457,0.00001480312,0.3211799,0.6616533,0.0110747,0.005283823,0.0003145428],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.4240258,0.0005314043,0.5694649,0.0003539752,0.0001407351,0.0005711803,0.00001888761,0.004477865,0.0004152856],"genre_scores_gemma":[0.9799204,0.0002470904,0.01901908,0.00001694235,0.0000646819,0.0003441332,0.00002355809,0.00006749308,0.0002965999],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.5558946,"threshold_uncertainty_score":0.9999282,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.009669850076948419,"score_gpt":0.2293974329823575,"score_spread":0.2197275829054091,"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."}}