{"id":"W2167522456","doi":"10.1109/icc.2007.843","title":"On the Maximum Useful Number of Receiver Antennas for MRC Diversity in Cochannel Interference and Noise","year":2007,"lang":"en","type":"article","venue":"","topic":"Advanced Wireless Communication Techniques","field":"Engineering","cited_by":8,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"","keywords":"Additive white Gaussian noise; Maximal-ratio combining; Interference (communication); Fading; Signal-to-noise ratio (imaging); Noise (video); Gaussian noise; Mathematics; Signal-to-interference ratio; Diversity gain; Signal-to-interference-plus-noise ratio; Bit error rate; Noise power; Antenna (radio); Carrier-to-noise ratio; Electronic engineering; Telecommunications; Topology (electrical circuits); Computer science; White noise; Power (physics); Statistics; Physics; Algorithm; Engineering; Decoding methods","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":[0.0001727793,0.00006152585,0.00008268321,0.00004029852,0.00003301169,0.000003371279,0.0001780193,0.00003647045,0.00002690571],"category_scores_gemma":[0.00003799535,0.00004794158,0.00001596168,0.00007704622,0.00004601001,0.00006659755,0.0001323067,0.00008945079,0.000002350597],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002784268,"about_ca_system_score_gemma":0.000001433658,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00003572087,"about_ca_topic_score_gemma":0.0001200024,"domain_scores_codex":[0.9996793,0.000008300309,0.0001093412,0.00006608844,0.00004339335,0.00009358387],"domain_scores_gemma":[0.9994411,0.0002729793,0.00002037868,0.0002138289,0.00003485301,0.00001682112],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.001585381,0.0007933687,0.2990715,0.0008710482,0.000256727,0.0000153967,0.02087334,0.005063578,0.09144511,0.3991556,0.008912108,0.1719569],"study_design_scores_gemma":[0.001901513,0.0002425978,0.1105958,0.0006574945,0.00002188543,0.000009643761,0.003524359,0.04906065,0.5866351,0.2452268,0.001215121,0.0009090038],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6599292,0.00002350908,0.3355378,0.00007087448,0.00002178987,0.0001765141,0.000004708666,0.00009754945,0.004138111],"genre_scores_gemma":[0.9934294,0.0001365494,0.006307757,0.00005158158,0.000002756124,0.000007018407,0.000001069977,0.000007568077,0.00005635316],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.49519,"threshold_uncertainty_score":0.1955001,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03278188871992526,"score_gpt":0.2734004889443687,"score_spread":0.2406186002244435,"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."}}