{"id":"W2012071412","doi":"10.1109/glocom.2013.6831630","title":"An efficient cross layer design for OFDMA-based wireless networks with channel reuse","year":2013,"lang":"en","type":"article","venue":"","topic":"Advanced Wireless Network Optimization","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"Carleton University","funders":"","keywords":"Computer science; Physical layer; Wireless network; Computer network; Scheduling (production processes); Mathematical optimization; Network topology; Wireless ad hoc network; Relay; Convex optimization; Wireless; Node (physics); Optimization problem; Channel allocation schemes; Channel (broadcasting); Distributed computing; Power (physics); Regular polygon; Algorithm; Telecommunications; Mathematics; Engineering","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":[],"consensus_categories":[],"category_scores_codex":[0.0001028219,0.0002315721,0.0001908365,0.00005920192,0.0001078034,0.0001045141,0.0002531114,0.000120393,0.00006252022],"category_scores_gemma":[0.000007369571,0.0001929301,0.00003376916,0.000237968,0.0000403906,0.0002286954,0.00001497655,0.0001050874,0.00001049041],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00006064704,"about_ca_system_score_gemma":0.00001504448,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000009960257,"about_ca_topic_score_gemma":0.000007019312,"domain_scores_codex":[0.9989274,0.00002238132,0.0002045811,0.0002648064,0.0001249481,0.0004558494],"domain_scores_gemma":[0.9990473,0.000114496,0.00004259232,0.0004903315,0.0001704044,0.0001348347],"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":[0.00004627049,0.00004383753,0.00008300415,0.00002429757,0.00001685193,7.374875e-7,0.00004307059,0.997421,0.0003916914,0.00006797041,0.0007948264,0.001066491],"study_design_scores_gemma":[0.0008163152,0.0001094129,0.0001849356,0.0000385954,0.0000110554,7.30939e-7,0.00001697682,0.9926472,0.005805871,0.00002891779,0.00003362667,0.0003063416],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.09506906,0.00006762661,0.9028943,0.00003604807,0.0001592614,0.000986143,0.00000327854,0.0007104052,0.00007384275],"genre_scores_gemma":[0.8645645,0.00001361257,0.1342961,0.0001024379,0.0001375702,0.0006715122,0.00003117046,0.0001111628,0.00007198899],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.7694954,"threshold_uncertainty_score":0.786746,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01363364831183395,"score_gpt":0.2312297796711591,"score_spread":0.2175961313593252,"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."}}