{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007313315,0.0006621769,0.0006836154,0.0003471589,0.0003552528,0.0008033552,0.001138533,0.0009008251,0.002356048],"category_scores_gemma":[0.001459921,0.0002821884,0.0004587224,0.0005780678,0.0004306659,0.0007739957,0.0008724374,0.0009087641,0.001974138],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003783586,"about_ca_system_score_gemma":0.0006462692,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0005342644,"about_ca_topic_score_gemma":0.0009362355,"domain_scores_codex":[0.9988281,0.000452967,0.00004698059,0.0001794724,0.0004064968,0.00008594749],"domain_scores_gemma":[0.999368,0.0002017899,0.00007939825,0.0001508917,0.0001726888,0.0000272551],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0008965984,0.0003743182,0.002430685,0.0004917164,0.0003054619,0.0008317882,0.0004863628,0.2787086,0.2691092,0.1010622,0.006265513,0.3390375],"study_design_scores_gemma":[0.000049343,0.0004064353,0.000260717,0.00002471171,0.00004605387,0.0007290928,0.00002051144,0.9504024,0.03714566,0.003564249,0.007306642,0.0000442118],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01091465,0.0002185367,0.9839594,0.0001226911,0.00006679487,0.00005706347,0.00004278983,0.0006898102,0.003928294],"genre_scores_gemma":[0.4573625,0.0003770227,0.5307949,0.0003008108,0.0001922337,0.0001785293,0.0001214673,0.00004813571,0.01062434],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002356048,"threshold_uncertainty_score":0.007881761,"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."}}