{"id":"W2101801661","doi":"10.1109/wcnc.2004.1311373","title":"On the performance of spatial multiplexing MIMO cellular systems with adaptive modulation and scheduling","year":2004,"lang":"en","type":"article","venue":"","topic":"Advanced MIMO Systems Optimization","field":"Engineering","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Manitoba","funders":"","keywords":"MIMO; Transmitter; Channel state information; Computer science; Singular value decomposition; Multiplexing; Link adaptation; Spectral efficiency; Spatial multiplexing; Scheduling (production processes); Minimum mean square error; Channel (broadcasting); Fading; Control theory (sociology); Electronic engineering; Algorithm; Telecommunications; Mathematics; Wireless; Engineering; Statistics; Mathematical optimization","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":[0.00007447624,0.00009503988,0.0001025818,0.00004105836,0.00005169027,0.00001193381,0.00003704042,0.00003506264,0.000001866569],"category_scores_gemma":[0.000008941757,0.00006353038,0.000009246891,0.00007776303,0.00002206966,0.0001405027,0.000006915189,0.00006619731,0.000002098184],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000498244,"about_ca_system_score_gemma":0.000006398484,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00009940564,"about_ca_topic_score_gemma":0.00001595514,"domain_scores_codex":[0.9995572,0.000009879179,0.0001480517,0.00009533551,0.00009472161,0.0000947938],"domain_scores_gemma":[0.9997342,0.00003901963,0.00004638165,0.0001169526,0.00004421926,0.00001919037],"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":[0.00001499304,0.000003888141,0.0001998096,0.00005447271,0.00001235341,3.110786e-7,0.0002046862,0.9871925,0.009638921,0.002541284,1.828445e-7,0.0001365921],"study_design_scores_gemma":[0.0003324953,0.00007698415,0.0001799478,0.0002154094,0.000004136984,0.000002117769,0.0002184741,0.9737836,0.02508444,0.00001962255,9.734844e-7,0.00008180886],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.4635546,0.0000475016,0.5358789,0.000003528862,0.00004231103,0.0001770944,8.824505e-7,0.00004990319,0.000245307],"genre_scores_gemma":[0.9844425,0.000008046823,0.01546383,0.000002484916,0.0000317256,0.00001556979,0.000002958132,0.00002087058,0.00001206268],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.5208879,"threshold_uncertainty_score":0.2590694,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.00936487862865177,"score_gpt":0.1785452247836063,"score_spread":0.1691803461549546,"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."}}