{"id":"W1974122131","doi":"10.1155/wcn/2006/75820","title":"Capacity Planning for Group-Mobility Users in OFDMA Wireless Networks","year":2006,"lang":"en","type":"article","venue":"EURASIP Journal on Wireless Communications and Networking","topic":"Advanced Wireless Network Optimization","field":"Engineering","cited_by":10,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"","keywords":"Computer science; Capacity planning; Orthogonal frequency-division multiple access; Microcell; Dimensioning; Computer network; Channel (broadcasting); Maximal-ratio combining; Channel capacity; Signal-to-interference ratio; Capacity utilization; Frequency-division multiple access; Interference (communication); Randomness; Wireless; Telecommunications; Orthogonal frequency-division multiplexing; Fading; Statistics; Mathematics","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":[0.001273029,0.0007678318,0.000748901,0.0006103517,0.0005520663,0.0009540583,0.0008962363,0.000652269,0.001462393],"category_scores_gemma":[0.004249427,0.0004513197,0.000352464,0.001036605,0.00130411,0.001165933,0.00109298,0.0007908778,0.0001711453],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001575518,"about_ca_system_score_gemma":0.001529268,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01040172,"about_ca_topic_score_gemma":0.006512798,"domain_scores_codex":[0.9990933,0.0004339242,0.00002202,0.00007641269,0.0001871916,0.0001870661],"domain_scores_gemma":[0.9979914,0.001518004,0.0001647097,0.00006239031,0.0001350503,0.0001284883],"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":[0.00003457084,0.00001713875,0.0001634385,0.00002123405,0.000008615045,0.0000561989,0.00005242762,0.9818526,0.0004302723,0.009296498,0.0003863683,0.007680594],"study_design_scores_gemma":[0.000005330614,0.000009144264,0.00006089631,0.000003374999,0.000003044531,0.0000096641,0.00001909787,0.9928067,0.000287372,0.006612225,0.0001788503,0.000004313134],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.09630871,0.0008506657,0.8942725,0.0005739948,0.00003502949,0.0001173646,0.00009698416,0.0002559045,0.007488858],"genre_scores_gemma":[0.9596229,0.0004103666,0.03817176,0.00005473739,0.0000276482,0.000161071,0.0000547647,0.00004219468,0.001454414],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01040172,"threshold_uncertainty_score":0.02068233,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02840580895671071,"score_gpt":0.2562951977997157,"score_spread":0.227889388843005,"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."}}