{"id":"W1653958936","doi":"10.1109/tsp.2015.2461511","title":"Spatial Reuse Precoding for Scalable Downlink Networks","year":2015,"lang":"en","type":"article","venue":"IEEE Transactions on Signal Processing","topic":"Advanced MIMO Systems Optimization","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"McMaster University; University of British Columbia","funders":"","keywords":"Precoding; Zero-forcing precoding; Telecommunications link; Computer science; MIMO; Cellular network; Single antenna interference cancellation; Kronecker product; Base station; Scalability; Channel (broadcasting); Algorithm; Kronecker delta; Computer network","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":[0.0002614778,0.0004249121,0.0002841631,0.0001668826,0.0002359684,0.0004125757,0.0003702524,0.0002643855,0.001708531],"category_scores_gemma":[0.001067398,0.0001489217,0.0002143379,0.0003637664,0.0004404738,0.0007045473,0.000491859,0.0005721876,0.0003917532],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000442396,"about_ca_system_score_gemma":0.0004700148,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001368454,"about_ca_topic_score_gemma":0.002364563,"domain_scores_codex":[0.999774,0.00005154701,0.000009328942,0.00003241187,0.00009837952,0.000034442],"domain_scores_gemma":[0.9996501,0.0001522308,0.00003640948,0.00007404605,0.00007322738,0.00001399327],"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":[0.0000656495,0.00003816032,0.0003405827,0.0001146565,0.00003037767,0.0002365009,0.0001094219,0.6779291,0.03530576,0.1558373,0.003434326,0.1265582],"study_design_scores_gemma":[0.00001237708,0.00006243821,0.0001078663,0.00001020333,0.000007363508,0.0000684313,0.0000196218,0.9684031,0.004273098,0.0232318,0.003795199,0.000008482867],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01487353,0.0002949515,0.9796045,0.0001513483,0.00004403068,0.00002541381,0.00004849411,0.0001644068,0.004793344],"genre_scores_gemma":[0.7247287,0.00113333,0.2681954,0.0002569712,0.0001829741,0.0001313133,0.0001621601,0.00004106593,0.005167984],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.001708531,"threshold_uncertainty_score":0.005715609,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02727550175885848,"score_gpt":0.2495435049201601,"score_spread":0.2222680031613016,"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."}}