{"id":"W2037693768","doi":"10.1007/s11277-006-9143-5","title":"Diversity Combining Options and Low-Complexity MMSE Equalization for Spread Spectrum OFDM Systems in Frequency Selective Fading Channels","year":2006,"lang":"en","type":"article","venue":"Wireless Personal Communications","topic":"Advanced Wireless Communication Techniques","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Alberta","funders":"","keywords":"Computer science; Fading; Orthogonal frequency-division multiplexing; Equalization (audio); Diversity scheme; Diversity combining; Subcarrier; Bit error rate; Algorithm; Minimum mean square error; Electronic engineering; Time diversity; Telecommunications; Channel (broadcasting); Mathematics; Statistics; Engineering","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":[0.0003167134,0.0002374075,0.0003189293,0.0003002292,0.00129173,0.00006753887,0.0007468351,0.0001367065,0.00000370631],"category_scores_gemma":[0.00003932079,0.0003045892,0.00006526674,0.0005090213,0.0002675665,0.0004565119,0.0005998535,0.0003888188,0.000002616081],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004320144,"about_ca_system_score_gemma":0.00002960247,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001798026,"about_ca_topic_score_gemma":0.00167041,"domain_scores_codex":[0.99865,0.0001696002,0.0004327498,0.00024994,0.0001633384,0.0003343589],"domain_scores_gemma":[0.9983368,0.000517218,0.0001333685,0.0008075841,0.0001400914,0.00006497477],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00002639167,0.0004245798,0.015426,0.0004367857,0.00008383841,0.000001621077,0.01033463,0.03513668,0.01520422,0.9216663,0.0002314672,0.001027474],"study_design_scores_gemma":[0.001218478,0.00007156694,0.01718677,0.0005139356,0.00004180051,0.00001616266,0.001805356,0.9184997,0.002130443,0.05731719,0.0003219298,0.0008766568],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8005164,0.00464085,0.1876299,0.0008131227,0.0001824622,0.001814683,0.0002620492,0.001398855,0.002741688],"genre_scores_gemma":[0.988575,0.0008993223,0.009661219,0.00001704128,0.00003905614,0.0004069098,0.0003204525,0.00004990042,0.00003105899],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.883363,"threshold_uncertainty_score":0.9999406,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04650192684113717,"score_gpt":0.2812056253786676,"score_spread":0.2347036985375304,"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."}}