{"id":"W2963476190","doi":"10.1109/glocom.2018.8647701","title":"Joint Doppler and Channel Estimation with Nested Arrays for Millimeter Wave Communications","year":2018,"lang":"en","type":"article","venue":"","topic":"Millimeter-Wave Propagation and Modeling","field":"Engineering","cited_by":6,"is_retracted":false,"has_abstract":true,"ca_institutions":"Concordia University","funders":"","keywords":"Computer science; Precoding; Channel (broadcasting); Extremely high frequency; Doppler effect; Computational complexity theory; Antenna (radio); Electronic engineering; Discretization; Antenna array; MIMO; Algorithm; Telecommunications; Mathematics; 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":[0.0003357217,0.0005317722,0.0004246971,0.0002140985,0.0002033923,0.0004074515,0.0004485687,0.0005201329,0.001062276],"category_scores_gemma":[0.001260453,0.0002958073,0.0004677064,0.0003867637,0.0003480074,0.0009384978,0.0007786107,0.0006713415,0.0003264703],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002164846,"about_ca_system_score_gemma":0.0004649264,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001445659,"about_ca_topic_score_gemma":0.002208262,"domain_scores_codex":[0.9997132,0.0001059716,0.00001254495,0.00005147505,0.00009171406,0.00002499132],"domain_scores_gemma":[0.9997035,0.0001431822,0.00004108091,0.00005047374,0.00004714205,0.00001449518],"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":[0.0001410598,0.00006474854,0.0009140124,0.0001022085,0.00006593522,0.0001273095,0.0001823526,0.7250788,0.03919477,0.03802831,0.001042875,0.1950576],"study_design_scores_gemma":[0.000004927709,0.00002565223,0.00008709519,0.000003699588,0.000006005088,0.00003797088,0.000009729259,0.9921517,0.00275276,0.00398549,0.0009263979,0.000008564123],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.002315974,0.00005371868,0.9971909,0.00002308876,0.00001034163,0.000004610928,0.000006536559,0.00007556622,0.0003192788],"genre_scores_gemma":[0.2414579,0.0003182977,0.7559773,0.00005704657,0.00005646924,0.0000643343,0.0000801272,0.00003400671,0.001954576],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.001445659,"threshold_uncertainty_score":0.003553689,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06702448622513853,"score_gpt":0.245343903158019,"score_spread":0.1783194169328805,"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."}}