{"id":"W1514055676","doi":"10.1109/itw.2008.4578651","title":"Asymptotic capacity and optimal precoding strategy of multi-level precode &amp;#x00026; forward in correlated channels","year":2008,"lang":"en","type":"preprint","venue":"","topic":"Cooperative Communication and Network Coding","field":"Computer Science","cited_by":14,"is_retracted":false,"has_abstract":true,"ca_institutions":"Concordia University; Institut National de la Recherche Scientifique","funders":"","keywords":"Precoding; Zero-forcing precoding; Computer science; Mathematics; Control theory (sociology); Mathematical optimization; Channel (broadcasting); MIMO; Telecommunications; 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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001831241,0.0008888482,0.000864051,0.0008098465,0.0004732905,0.001309628,0.0009961644,0.000952508,0.001951245],"category_scores_gemma":[0.009175234,0.0005925137,0.0005465278,0.0008118659,0.002405627,0.001595857,0.001358379,0.0009108264,0.0004309955],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002059398,"about_ca_system_score_gemma":0.001661275,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004723657,"about_ca_topic_score_gemma":0.002763323,"domain_scores_codex":[0.9990139,0.00034656,0.00002738342,0.00009169179,0.0002665909,0.0002538076],"domain_scores_gemma":[0.9934421,0.004459654,0.0006748327,0.0002954183,0.0009583161,0.0001697347],"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":[0.00006431939,0.00002974704,0.0004450329,0.00006174896,0.00002591136,0.0001627059,0.0001122638,0.9346359,0.00251199,0.05744551,0.0006783657,0.003826483],"study_design_scores_gemma":[0.000005886799,0.00001377167,0.0001550473,0.00001117626,0.000006111689,0.00003346951,0.00002434557,0.9853596,0.0005363331,0.0137602,0.00008458042,0.000009453403],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.318285,0.001204384,0.6516123,0.0009393396,0.00005085444,0.00007328257,0.0003712389,0.0003982698,0.02706544],"genre_scores_gemma":[0.9893125,0.0003240605,0.008500597,0.00006069587,0.00002808603,0.0000640949,0.00009121415,0.00004017767,0.00157854],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.004723657,"threshold_uncertainty_score":0.01494205,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1922076030966851,"score_gpt":0.3170137894176999,"score_spread":0.1248061863210148,"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."}}