{"id":"W4297336502","doi":"10.48550/arxiv.1606.02316","title":"Decentralized AP Selection in Large-Scale Wireless LANs Considering\\n Multi-AP Interference","year":2016,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Wireless Networks and Protocols","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Queen's University","funders":"","keywords":"Telecommunications link; Interference (communication); Computer science; Signal-to-interference-plus-noise ratio; Throughput; Computer network; Selection algorithm; Channel (broadcasting); Signal-to-noise ratio (imaging); Wireless network; Selection (genetic algorithm); Wireless; Telecommunications; Power (physics); Physics","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":[0.001189342,0.0006088904,0.001117913,0.0004731892,0.001226441,0.001082863,0.001660957,0.0007272519,0.000718103],"category_scores_gemma":[0.002266414,0.0004610052,0.0003034001,0.0007928492,0.0009203198,0.001413031,0.001419932,0.000654049,0.0002266668],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008817947,"about_ca_system_score_gemma":0.001034343,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002948759,"about_ca_topic_score_gemma":0.004567393,"domain_scores_codex":[0.9989762,0.0003894411,0.00003098221,0.0002087695,0.0002332024,0.0001615002],"domain_scores_gemma":[0.9983208,0.0008133859,0.000270373,0.0002078352,0.0002508916,0.0001367281],"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":[0.0002709707,0.0001401355,0.002865938,0.0001166155,0.00007775675,0.0005839565,0.0002070427,0.8984624,0.01348155,0.01105298,0.001945495,0.07079516],"study_design_scores_gemma":[0.00002026308,0.00006874597,0.000399693,0.000002517404,0.00001695456,0.0000811339,0.00005225356,0.9962592,0.0006983778,0.001989852,0.0004029846,0.000008053196],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1368371,0.00111481,0.8568931,0.0003653859,0.0001126059,0.00008907591,0.00004390192,0.0007578857,0.00378612],"genre_scores_gemma":[0.9704485,0.0002206274,0.02814807,0.00003736334,0.00007494118,0.00004552301,0.00003340227,0.00001321567,0.000978403],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002948759,"threshold_uncertainty_score":0.006397843,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06630661042503136,"score_gpt":0.2218203372557291,"score_spread":0.1555137268306978,"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."}}