{"id":"W2618047800","doi":"10.1109/twc.2017.2707549","title":"Multi-Resolution Multicasting Over the Grassmann and Stiefel Manifolds","year":2017,"lang":"en","type":"article","venue":"IEEE Transactions on Wireless Communications","topic":"Advanced Wireless Communication Techniques","field":"Engineering","cited_by":10,"is_retracted":false,"has_abstract":true,"ca_institutions":"Carleton University","funders":"Qatar National Research Fund; Ontario Ministry of Economic Development and Innovation","keywords":"Grassmannian; Stiefel manifold; Constellation; Channel state information; Algorithm; Computer science; MIMO; Multicast; Decoding methods; Topology (electrical circuits); Mathematics; Theoretical computer science; Channel (broadcasting); Telecommunications; Wireless; Pure mathematics; 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":[0.0005649983,0.0004451245,0.0003819295,0.0004219713,0.0003045567,0.0005072068,0.0005118184,0.00054363,0.0006234163],"category_scores_gemma":[0.001643697,0.0001883007,0.0003172492,0.0004440338,0.000506584,0.0009272869,0.0006228247,0.000622649,0.000212487],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006131323,"about_ca_system_score_gemma":0.0004378955,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0004665992,"about_ca_topic_score_gemma":0.0004796633,"domain_scores_codex":[0.9995456,0.0001583028,0.00001523918,0.00005511771,0.0001686571,0.00005710978],"domain_scores_gemma":[0.9994605,0.0002108598,0.0001059857,0.00007808623,0.0001089632,0.00003545515],"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":[0.0001117935,0.00006297572,0.0009267859,0.0001115458,0.00003104416,0.0002689585,0.0002415806,0.5876761,0.0393631,0.2646751,0.001295477,0.1052356],"study_design_scores_gemma":[0.000005918737,0.00007187251,0.0001227013,0.000005449103,0.000003166163,0.00007402017,0.0000182058,0.9693742,0.004403769,0.02465407,0.001255819,0.00001075568],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.06092091,0.0002330232,0.9355173,0.0001381724,0.00002119456,0.00003425844,0.00002849365,0.00007752012,0.003029171],"genre_scores_gemma":[0.694777,0.0003431775,0.3028139,0.00006323391,0.0000368158,0.00007299846,0.0000488741,0.00002298493,0.001821051],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.0006234163,"threshold_uncertainty_score":0.004448652,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04604218878682856,"score_gpt":0.3041596102271333,"score_spread":0.2581174214403047,"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."}}