{"id":"W4289824661","doi":"10.1109/lcomm.2022.3195410","title":"Spread Unsourced Random Access With an Iterative MIMO Receiver","year":2022,"lang":"en","type":"article","venue":"IEEE Communications Letters","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"Dalhousie University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Preamble; Payload (computing); Computer science; Random access; Network packet; Channel (broadcasting); MIMO; Single antenna interference cancellation; Interference (communication); Algorithm; Real-time computing; Computer network","routes":{"ca_aff":true,"ca_fund":true,"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":[0.0001152468,0.0001338351,0.0001405909,0.0001765565,0.000519185,0.00009191696,0.001504195,0.0000319037,0.00009643318],"category_scores_gemma":[0.00001047623,0.0001310789,0.00003472634,0.0004749928,0.000153081,0.0003188078,0.0001828675,0.0003608818,0.00001159577],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001144804,"about_ca_system_score_gemma":0.000011608,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00004894332,"about_ca_topic_score_gemma":0.00006445115,"domain_scores_codex":[0.999218,0.0001509663,0.0001714915,0.0001372124,0.0001479024,0.000174379],"domain_scores_gemma":[0.9984104,0.0001058257,0.00004330562,0.001369452,0.00003784062,0.00003318112],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0002016527,0.000186996,0.00308273,0.00003770981,0.0002829079,0.00001754985,0.00812833,0.9120931,0.02937422,0.001764135,0.03611869,0.008711959],"study_design_scores_gemma":[0.01123807,0.0005445007,0.003554471,0.0001247041,0.0002630783,0.0001355462,0.007681584,0.4283966,0.09747314,0.0007772216,0.4469122,0.002898967],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.845269,0.0004554288,0.1341917,0.008986901,0.0004553151,0.0009053425,0.000108897,0.002946277,0.00668113],"genre_scores_gemma":[0.9954671,0.00006937132,0.002688738,0.001254437,0.00001794084,0.0002994569,0.0001125192,0.00003690201,0.00005358949],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4836966,"threshold_uncertainty_score":0.534524,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02483214184869886,"score_gpt":0.2590982576589669,"score_spread":0.234266115810268,"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."}}