{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001042201,0.0001867151,0.000168548,0.0001686013,0.0002030089,0.00003107436,0.001163113,0.00007280046,0.00001193961],"category_scores_gemma":[0.0000160857,0.0001920019,0.00005149264,0.0002480138,0.0001833367,0.0003753911,0.00009009815,0.0003016031,0.000007665588],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001787072,"about_ca_system_score_gemma":0.000008800502,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00009380785,"about_ca_topic_score_gemma":0.0006994611,"domain_scores_codex":[0.9991946,0.00005934346,0.0003070098,0.0001717157,0.00007004759,0.0001972759],"domain_scores_gemma":[0.9977319,0.0002021947,0.0001341338,0.001759986,0.0001239163,0.00004781747],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0001596558,0.0001570803,0.0008909855,0.0001561535,0.0002280688,8.492053e-7,0.004877681,0.004935142,0.7802986,0.002228507,0.0008690469,0.2051982],"study_design_scores_gemma":[0.001071791,0.0002499764,0.001821575,0.0005067192,0.0001079639,0.000009882498,0.0004500104,0.249791,0.737373,0.001153568,0.006405483,0.001058986],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.04266662,0.0002574214,0.9544201,0.0004069744,0.0001291119,0.0005531412,0.00001187716,0.0008592144,0.0006954747],"genre_scores_gemma":[0.9133951,0.0004869699,0.08465344,0.0001816978,0.00002293476,0.001151508,0.00003667219,0.00005720072,0.00001444384],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.8707285,"threshold_uncertainty_score":0.7829611,"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."}}