{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008703567,0.000890704,0.0009153547,0.0005883314,0.0004368692,0.0009280253,0.00102186,0.0005901063,0.001664283],"category_scores_gemma":[0.002493493,0.0002625926,0.0004730909,0.0007963129,0.0005366266,0.001011896,0.000729448,0.0006098729,0.001006021],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005074329,"about_ca_system_score_gemma":0.0008486822,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001230387,"about_ca_topic_score_gemma":0.001800234,"domain_scores_codex":[0.9986647,0.0004375063,0.00006384142,0.0002023632,0.0004618547,0.0001698397],"domain_scores_gemma":[0.9990545,0.0003711884,0.0001142208,0.0001463762,0.0002674786,0.00004624038],"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":[0.0006197795,0.0001986563,0.002116543,0.0001479865,0.0001016712,0.0001624077,0.0001359981,0.4735631,0.04150745,0.01834561,0.005462023,0.4576387],"study_design_scores_gemma":[0.00003846231,0.0001089743,0.0003700499,0.000006362277,0.00002001952,0.0001307721,0.00001882242,0.9844354,0.00988423,0.003373912,0.001591577,0.00002142502],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01429856,0.0003169892,0.981926,0.0001043191,0.00004526533,0.00004392147,0.00005055687,0.0005277591,0.0026867],"genre_scores_gemma":[0.5953368,0.0006102971,0.3965234,0.0002806477,0.0001301751,0.0002141909,0.0003334176,0.0001121725,0.006458788],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.001664283,"threshold_uncertainty_score":0.005567551,"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."}}