{"id":"W2160956890","doi":"10.1109/cnsr.2005.52","title":"Rayleigh Flat Fading Channels’ Capacity","year":2005,"lang":"en","type":"article","venue":"","topic":"Advanced Wireless Network Optimization","field":"Engineering","cited_by":50,"is_retracted":false,"has_abstract":true,"ca_institutions":"Carleton University","funders":"","keywords":"Rayleigh fading; Fading; Fading distribution; Channel state information; Additive white Gaussian noise; Channel capacity; Channel (broadcasting); Independent and identically distributed random variables; Computer science; Rayleigh scattering; Transmission (telecommunications); Mathematics; Telecommunications; Topology (electrical circuits); Statistics; Electronic engineering; Random variable; Physics; Engineering; Wireless; Combinatorics; Optics","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":[0.00005163061,0.0001431399,0.0001277683,0.00006258004,0.00005860495,0.00002453177,0.00009067894,0.00007809566,0.000397625],"category_scores_gemma":[0.00001001754,0.0001474535,0.00003591824,0.0001870167,0.00001634065,0.0003182075,0.00001823201,0.0001290337,0.0003144182],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001101159,"about_ca_system_score_gemma":0.000002684198,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000003239937,"about_ca_topic_score_gemma":0.00003683083,"domain_scores_codex":[0.9993024,0.00000809167,0.0001625919,0.0001418003,0.0001022826,0.0002827868],"domain_scores_gemma":[0.9996757,0.00002800921,0.00001684385,0.0001818229,0.0000245737,0.00007307431],"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":[0.000001224213,0.000003294016,0.0000481765,0.000008412112,0.000009388956,3.023063e-7,0.0001545954,0.9895355,0.001916532,0.0007172059,0.00195234,0.005653036],"study_design_scores_gemma":[0.0002031077,0.00000450443,0.00004028899,0.00001615903,0.000005409926,0.000005401876,0.00001045207,0.9300991,0.01235589,0.0001988064,0.05680399,0.0002569378],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1126836,0.0001428141,0.8589925,0.00007673961,0.000436018,0.0001128067,0.000001575134,0.001073585,0.02648037],"genre_scores_gemma":[0.9097632,0.0001076927,0.08814194,0.00007022123,0.0005233872,0.00001195809,0.0000123154,0.00004476163,0.001324593],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.7970796,"threshold_uncertainty_score":0.6012979,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0177663602656419,"score_gpt":0.2107839778143332,"score_spread":0.1930176175486913,"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."}}