{"id":"W2164332939","doi":"10.1109/icassp.2012.6288559","title":"An incremental Grassmannian feedback scheme for linearly precoded spatial multiplexing MIMO systems","year":2012,"lang":"en","type":"article","venue":"","topic":"Advanced MIMO Systems Optimization","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"McMaster University","funders":"","keywords":"Grassmannian; Codebook; MIMO; Precoding; Transmitter; Spatial multiplexing; Fading; Vector quantization; Computer science; Quantization (signal processing); Spatial correlation; Algorithm; Subspace topology; Rayleigh fading; Multiplexing; Block (permutation group theory); Mathematics; Topology (electrical circuits); Theoretical computer science; Decoding methods; Channel (broadcasting); Telecommunications; Artificial intelligence","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":[0.0002267879,0.0002154048,0.0002208832,0.0000911246,0.00008884971,0.00007462631,0.0001369477,0.0001243797,0.00002701039],"category_scores_gemma":[0.00002885314,0.0002211599,0.00005238404,0.0001004322,0.0000134431,0.0009491941,0.00001867651,0.0000881813,0.00004570701],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001540403,"about_ca_system_score_gemma":0.000007851604,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002114199,"about_ca_topic_score_gemma":0.00009962358,"domain_scores_codex":[0.9988233,0.00002620847,0.0003790324,0.0001847126,0.0001207589,0.0004659419],"domain_scores_gemma":[0.9993757,0.00004099335,0.00006009039,0.0002661742,0.00007273776,0.0001843216],"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":[0.00005083444,0.0001471463,0.01132938,0.0005742281,0.0001294805,5.910674e-7,0.001103033,0.7906373,0.1911292,0.001105366,0.0008159669,0.002977456],"study_design_scores_gemma":[0.0006691297,0.00005563236,0.000349065,0.00004426798,0.00001152868,0.00000539448,0.00040452,0.9781263,0.0189477,0.000005598723,0.001055544,0.0003253389],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.05574011,0.0002126272,0.9399706,0.000005637345,0.001240106,0.001039415,0.0000426334,0.0007625496,0.0009863428],"genre_scores_gemma":[0.872622,0.000003506667,0.1259616,0.00000858267,0.0008249796,0.0001821689,0.000156777,0.00008714336,0.0001532437],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.8168818,"threshold_uncertainty_score":0.9018639,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01966635233657318,"score_gpt":0.2546601651087249,"score_spread":0.2349938127721517,"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."}}