{"id":"W2951958665","doi":"10.48550/arxiv.1903.02077","title":"Generalized Approximate Message Passing for Massive MIMO mmWave Channel Estimation with Laplacian Prior","year":2019,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Millimeter-Wave Propagation and Modeling","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"Natural Sciences and Engineering Research Council of Canada; Canada Research Chairs","keywords":"Message passing; Computer science; Algorithm; MIMO; Expectation propagation; Channel (broadcasting); Bayesian inference; Bayesian probability; Theoretical computer science; Gaussian; Artificial intelligence; Gaussian process; Telecommunications","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":[0.001443719,0.0009152369,0.0009261907,0.0006421963,0.0004248937,0.0008751458,0.001208,0.001016278,0.001438364],"category_scores_gemma":[0.005856387,0.0004897462,0.0006386102,0.00104473,0.001154912,0.001675118,0.001403591,0.001375799,0.0005059114],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000887865,"about_ca_system_score_gemma":0.001099078,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003905211,"about_ca_topic_score_gemma":0.00352059,"domain_scores_codex":[0.9991954,0.0003395733,0.00003327484,0.0001195828,0.0002467964,0.00006541013],"domain_scores_gemma":[0.9981627,0.001270046,0.0001396829,0.0001831806,0.000196002,0.0000484728],"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":[0.00006031752,0.00002306849,0.0002553363,0.00007707639,0.00003243124,0.00006446592,0.00009611066,0.8930674,0.001748145,0.05059979,0.001041251,0.05293471],"study_design_scores_gemma":[0.000004108827,0.00001186111,0.00003677873,0.000003992847,0.000003315649,0.00001267141,0.000005326994,0.9840323,0.0004121832,0.01507017,0.000401885,0.000005385994],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.002288664,0.0000685065,0.9970396,0.00008527561,0.00001092064,0.000007709856,0.00001772573,0.00008889333,0.0003925966],"genre_scores_gemma":[0.4060833,0.0007954109,0.5865628,0.0003328852,0.0001818932,0.0002460813,0.0003610914,0.0001381144,0.005298331],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.003905211,"threshold_uncertainty_score":0.007764935,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05607765175520941,"score_gpt":0.1857642239891421,"score_spread":0.1296865722339327,"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."}}