{"id":"W2078250388","doi":"10.1109/lcomm.2011.110711.111195","title":"ML Detection with Successive Group Interference Cancellation for Interleaved OFDMA Uplink","year":2011,"lang":"en","type":"article","venue":"IEEE Communications Letters","topic":"Advanced Wireless Communication Techniques","field":"Engineering","cited_by":10,"is_retracted":false,"has_abstract":true,"ca_institutions":"Western University","funders":"","keywords":"Single antenna interference cancellation; Telecommunications link; Interference (communication); Computer science; Minimum mean square error; Algorithm; Orthogonal frequency-division multiplexing; Frequency-division multiple access; Decoding methods; Multiuser detection; Multiplexing; Frequency domain; Electronic engineering; Channel (broadcasting); Mathematics; Telecommunications; Code division multiple access; 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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001437468,0.000786482,0.0008660891,0.0006530691,0.0004321628,0.0006913109,0.0009423757,0.0007658986,0.0007635214],"category_scores_gemma":[0.004879162,0.000487929,0.0004992166,0.0008232163,0.0008690635,0.001003798,0.001183217,0.0008210384,0.0004784625],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005009018,"about_ca_system_score_gemma":0.001173665,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001577238,"about_ca_topic_score_gemma":0.002267766,"domain_scores_codex":[0.9982479,0.0006541516,0.00007214739,0.0001449865,0.0007601706,0.0001207911],"domain_scores_gemma":[0.9980903,0.001056317,0.0002585971,0.0002191791,0.0003164935,0.00005910184],"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":[0.0008850464,0.0001627947,0.002114402,0.0003032001,0.0002086181,0.0003801105,0.0003041545,0.5419812,0.06999521,0.0321628,0.001763375,0.3497391],"study_design_scores_gemma":[0.00002964222,0.00009440551,0.0001895528,0.000006590073,0.00001712069,0.00009995534,0.000009869219,0.9848109,0.01134111,0.002731628,0.0006503158,0.0000188205],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01348831,0.00023233,0.985105,0.00008416908,0.00002305046,0.0000194956,0.00001496049,0.0003290058,0.0007037025],"genre_scores_gemma":[0.5764496,0.0003470926,0.4215069,0.0001570631,0.00007972553,0.00008867485,0.00008655479,0.00003391222,0.001250607],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.001577238,"threshold_uncertainty_score":0.007602155,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03942410910410303,"score_gpt":0.2527048922235443,"score_spread":0.2132807831194413,"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."}}