{"id":"W2169032273","doi":"10.1109/glocom.2005.1578205","title":"Reactive cognitive radio algorithms for co-existence between IEEE 802.11b and 802.16a networks","year":2005,"lang":"en","type":"article","venue":"GLOBECOM '05. IEEE Global Telecommunications Conference, 2005.","topic":"Advanced Wireless Network Optimization","field":"Engineering","cited_by":55,"is_retracted":false,"has_abstract":true,"ca_institutions":"Nortel (Canada)","funders":"","keywords":"Cognitive radio; Computer science; Computer network; Throughput; Radio resource management; Interference (communication); Co-channel interference; Wireless; Frequency band; IEEE 802.11; Power control; Wireless network; Channel (broadcasting); Telecommunications; Power (physics); Bandwidth (computing)","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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0003669766,0.0006836803,0.0007904543,0.0001635907,0.0006390926,0.0002229876,0.001026173,0.0004602801,0.00005663908],"category_scores_gemma":[0.00008230063,0.0008043111,0.0001688623,0.0006682728,0.000436101,0.0008634228,0.0001190935,0.0006921151,0.00006742303],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007013607,"about_ca_system_score_gemma":0.0001638281,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00006492568,"about_ca_topic_score_gemma":0.0004594185,"domain_scores_codex":[0.9967887,0.000183944,0.0009254439,0.0006871099,0.000299491,0.001115319],"domain_scores_gemma":[0.9969112,0.0007165004,0.0003212374,0.001130959,0.0005049227,0.0004151563],"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":[0.0001369616,0.0003409569,0.003326877,0.00009109481,0.0008580809,0.000006128385,0.0005179445,0.5287677,0.0001106472,0.004332026,0.03094354,0.430568],"study_design_scores_gemma":[0.002488689,0.000164549,0.003692941,0.000275127,0.0002890377,0.00006862845,0.0003458204,0.9447983,0.0006433057,0.001282919,0.04455959,0.001391092],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01637555,0.002555485,0.9601441,0.0006390682,0.0005982668,0.001937883,0.001400879,0.0009905136,0.01535825],"genre_scores_gemma":[0.894277,0.004166739,0.09875664,0.0002016588,0.0006612635,0.0004826867,0.001120999,0.00009794971,0.0002351001],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.8779014,"threshold_uncertainty_score":0.9994408,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03399956503745423,"score_gpt":0.2982001884013845,"score_spread":0.2642006233639302,"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."}}