{"id":"W2126252670","doi":"10.1110/tcomm.2010.062510.080615","title":"SER Analysis and PDF Derivation for Multi-Hop Amplify-and-Forward Relay Systems","year":2010,"lang":"en","type":"article","venue":"IEEE Transactions on Communications","topic":"Cooperative Communication and Network Coding","field":"Computer Science","cited_by":19,"is_retracted":false,"has_abstract":true,"ca_institutions":"Queen's University","funders":"","keywords":"Moment-generating function; Relay; Hop (telecommunications); Random variable; Cumulative distribution function; Probability density function; Erlang (programming language); Expression (computer science); Mathematics; Topology (electrical circuits); Probability of error; Bit error rate; Computer science; Algorithm; Telecommunications; Statistics; Combinatorics; Physics; Decoding methods; Theoretical computer science; Quantum mechanics","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":[],"consensus_categories":[],"category_scores_codex":[0.0004575008,0.0001553918,0.0002140813,0.0003406091,0.001228697,0.0002847611,0.001058285,0.0001131827,0.00002212246],"category_scores_gemma":[0.00002912652,0.0001575694,0.00009450444,0.000789473,0.0001557854,0.0004051247,0.00003258464,0.0004799773,0.00002662004],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002886708,"about_ca_system_score_gemma":0.00003975625,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00004507058,"about_ca_topic_score_gemma":0.001545712,"domain_scores_codex":[0.9988896,0.0001868018,0.0003311243,0.0003017719,0.0001154023,0.0001752973],"domain_scores_gemma":[0.996433,0.000804383,0.00011466,0.00227723,0.0002519055,0.0001188232],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00008515131,0.002360511,0.001612847,0.000137436,0.002913998,9.96563e-7,0.01103459,0.03885112,0.04581479,0.3661554,0.001189044,0.5298441],"study_design_scores_gemma":[0.0005001738,0.00004325273,0.001940504,0.00001673711,0.0001446251,0.000007139646,0.00009168668,0.9740248,0.0007326013,0.0001084238,0.02215015,0.0002399055],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.003445456,0.0002810932,0.9924715,0.002655625,0.0002298465,0.0004486695,0.00001888182,0.0001514062,0.0002975728],"genre_scores_gemma":[0.864588,0.002097983,0.1320212,0.0001810958,0.00001213567,0.0003516423,0.00001735695,0.00001141566,0.0007191645],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9351737,"threshold_uncertainty_score":0.9450268,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06593631995843402,"score_gpt":0.3211921444500576,"score_spread":0.2552558244916236,"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."}}