{"id":"W1871031609","doi":"10.1007/11946441_34","title":"Interference Aware Dynamic Subchannel Allocation in a Multi-cellular OFDMA System Based on Traffic Situation","year":2006,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Advanced Wireless Network Optimization","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":false,"ca_institutions":"Queen's University","funders":"","keywords":"Channel allocation schemes; Quality of service; Computer science; Telecommunications link; Interference (communication); Orthogonal frequency-division multiple access; Computer network; Bandwidth (computing); Orthogonal frequency-division multiplexing; Dynamic bandwidth allocation; WiMAX; Adaptability; Bandwidth allocation; Channel (broadcasting); Frequency-division multiple access; Spectral efficiency; Real-time computing; Telecommunications; Wireless","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":[0.0002047188,0.0003662343,0.0005919258,0.0002351365,0.0004523816,0.0005987218,0.0004536093,0.0002792485,0.0007325131],"category_scores_gemma":[0.0007171797,0.0002127406,0.0001863558,0.0003783999,0.0003252401,0.0004280538,0.0003210663,0.000342494,0.0001408182],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003477305,"about_ca_system_score_gemma":0.0002732398,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001308571,"about_ca_topic_score_gemma":0.002827162,"domain_scores_codex":[0.9998426,0.00004028925,0.000006341457,0.00003180911,0.00004126596,0.00003767445],"domain_scores_gemma":[0.9996868,0.0001512333,0.00002337635,0.00002382621,0.000086452,0.00002840252],"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":[0.0007459764,0.0001527278,0.003569977,0.00012263,0.0001062853,0.0007554697,0.0002815174,0.6120225,0.2025103,0.01091439,0.001743271,0.1670749],"study_design_scores_gemma":[0.000006119109,0.00005427486,0.001253144,0.000003730504,0.00003239,0.0001850718,0.00002693561,0.991727,0.004580582,0.001840782,0.0002761027,0.00001389637],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3661265,0.001290984,0.6208298,0.0002997537,0.0001505757,0.0000416192,0.00008402565,0.0004708292,0.01070601],"genre_scores_gemma":[0.9770569,0.0002304959,0.02142586,0.00003076657,0.00004221936,0.0000134482,0.00002520076,0.00001903952,0.001156098],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.001308571,"threshold_uncertainty_score":0.002601862,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0104139913745345,"score_gpt":0.2092997407637786,"score_spread":0.1988857493892441,"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."}}