{"id":"W4233047538","doi":"10.1109/glocom.2014.7417434","title":"Effective Data Rate Based Rank Adaptive Receive Antenna Selection","year":2014,"lang":"en","type":"article","venue":"2015 IEEE Global Communications Conference (GLOBECOM)","topic":"Advanced MIMO Systems Optimization","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"Ontario Tech University","funders":"","keywords":"Computer science; Beamforming; Telecommunications link; Physical layer; Subspace topology; Antenna (radio); Interference (communication); Signal subspace; Selection algorithm; Channel (broadcasting); Algorithm; Spectral efficiency; Selection (genetic algorithm); Adaptive beamformer; Electronic engineering; Real-time computing; Computer network; Wireless; Telecommunications; Engineering; 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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0007478818,0.0003672832,0.0004179127,0.0001046627,0.0003194644,0.0001200539,0.002068619,0.0001953671,0.00005740508],"category_scores_gemma":[0.0003197098,0.0004057334,0.00006169254,0.0007543395,0.0001855882,0.0008200296,0.0002850897,0.0003935172,0.0002751076],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000490543,"about_ca_system_score_gemma":0.0001263552,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002864852,"about_ca_topic_score_gemma":0.001687152,"domain_scores_codex":[0.9976876,0.0007467914,0.0004893704,0.0004775147,0.0001828468,0.0004158477],"domain_scores_gemma":[0.9953355,0.0004032168,0.0001958062,0.003277105,0.0006260503,0.0001622884],"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":[0.0004647653,0.0007215776,0.003751952,0.000341198,0.0009008435,0.000003981314,0.0006997638,0.6939843,0.01242096,0.04349484,0.05458278,0.1886331],"study_design_scores_gemma":[0.0008025641,0.00007660499,0.001271827,0.0001124153,0.00006265543,0.000007528028,0.00009580452,0.9888521,0.000376677,0.0009136576,0.007004799,0.0004233982],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.0006895017,0.0003980515,0.9772256,0.0003434113,0.0005549836,0.0009713423,0.0003818174,0.0007947765,0.0186405],"genre_scores_gemma":[0.9599897,0.0002852446,0.03864737,0.00009990347,0.00008291122,0.0001849515,0.0006152369,0.00004259726,0.00005209964],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9593002,"threshold_uncertainty_score":0.9998394,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04679741087146937,"score_gpt":0.2987096779602413,"score_spread":0.2519122670887719,"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."}}