{"id":"W2080699736","doi":"10.1109/glocom.2006.794","title":"WLC31-2: Capacity of MIMO Rician Fading Channels with Transmitter and Receiver Channel State Information","year":2006,"lang":"en","type":"article","venue":"Globecom","topic":"Advanced MIMO Systems Optimization","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Institut National de la Recherche Scientifique; Université du Québec à Montréal","funders":"","keywords":"Rician fading; MIMO; Transmitter; Channel state information; Fading; Channel (broadcasting); Channel capacity; Computer science; Ergodic theory; Topology (electrical circuits); Precoding; Mathematics; Telecommunications; Electronic engineering; Control theory (sociology); Wireless; Engineering; Mathematical analysis","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":[0.00005877332,0.0001206987,0.0001526964,0.00009515822,0.00003421712,0.00002051838,0.00004460269,0.00004685519,0.000006079436],"category_scores_gemma":[0.000002778672,0.000113697,0.00001678132,0.0001507319,0.0000251177,0.0006572118,0.000005812713,0.00006368909,0.000006414324],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004486886,"about_ca_system_score_gemma":0.000003945446,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002377119,"about_ca_topic_score_gemma":0.00009977346,"domain_scores_codex":[0.9994359,0.00001010592,0.0002296066,0.00007778999,0.00008200164,0.0001645944],"domain_scores_gemma":[0.9997451,0.00001146976,0.00006277659,0.00009684132,0.00005311484,0.00003072567],"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":[0.00001209357,0.000007005676,0.000573013,0.0001866584,0.00001852535,8.344368e-7,0.001787935,0.9943284,0.0005551355,0.00008257956,0.0002485902,0.002199245],"study_design_scores_gemma":[0.00400658,0.0002305907,0.01900869,0.0006303433,0.00008162613,0.00007022926,0.0007416976,0.9189901,0.04363314,0.002495987,0.008738018,0.001372999],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3394445,0.00008462965,0.6569293,0.00002670309,0.0001516519,0.0002389562,0.00003884603,0.0001383044,0.002947144],"genre_scores_gemma":[0.9963675,0.00002162259,0.003458503,0.0000195834,0.00003392849,0.000011312,0.00002546246,0.00001553914,0.00004659694],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.6569229,"threshold_uncertainty_score":0.463643,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.005304989565263642,"score_gpt":0.1677155270355007,"score_spread":0.162410537470237,"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."}}