{"id":"W2170734840","doi":"10.1109/tcomm.2009.06.070109","title":"On the capacity of log-normal fading channels","year":2009,"lang":"en","type":"article","venue":"IEEE Transactions on Communications","topic":"Advanced Wireless Communication Techniques","field":"Engineering","cited_by":50,"is_retracted":false,"has_abstract":true,"ca_institutions":"Institut National de la Recherche Scientifique; Ericsson (Canada)","funders":"","keywords":"Log-normal distribution; Fading; Truncation (statistics); Expression (computer science); Mathematics; Channel capacity; Series (stratigraphy); Ergodic theory; Remainder; Channel (broadcasting); Maximal-ratio combining; Statistics; Random variable; Fading distribution; Algorithm; Applied mathematics; Computer science; Telecommunications; Mathematical analysis; Rayleigh fading; Decoding methods","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":[0.0001698029,0.0001481942,0.0001542527,0.000158838,0.0003799043,0.00001742504,0.001218583,0.00007925064,0.00004940212],"category_scores_gemma":[0.00001141503,0.0001329172,0.0001005911,0.0003756361,0.0001652527,0.0001505982,0.000003748162,0.0006052958,0.00002840655],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00008246658,"about_ca_system_score_gemma":0.00001066606,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001190309,"about_ca_topic_score_gemma":0.00002142033,"domain_scores_codex":[0.9991935,0.0001105484,0.0003002089,0.00009524178,0.0001372977,0.0001631865],"domain_scores_gemma":[0.9966857,0.0005948674,0.00006191447,0.002543337,0.00007152063,0.00004259184],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0000236794,0.0007504363,0.000002470487,0.00002361614,0.0001149689,2.843641e-7,0.00223705,0.7724037,0.04179013,0.1147654,0.001076993,0.06681133],"study_design_scores_gemma":[0.000416544,0.0003127934,0.000190519,0.0002808598,0.00005691396,0.00001040458,0.0002921244,0.1740498,0.7873378,0.0309536,0.005505306,0.0005933142],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01715372,0.0001326908,0.9646451,0.002585842,0.0001285908,0.0003629997,0.000043228,0.0007861175,0.01416164],"genre_scores_gemma":[0.9913865,0.0009969844,0.007222171,0.0001844759,0.000007251549,0.0001110502,0.000004037211,0.00002200765,0.00006554281],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9742327,"threshold_uncertainty_score":0.5420207,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04269886501188365,"score_gpt":0.2688345528964887,"score_spread":0.2261356878846051,"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."}}