{"id":"W2776370065","doi":"10.1109/tgcn.2017.2786704","title":"Optimal Relay Selection and Power Control for Energy-Harvesting Wireless Relay Networks","year":2017,"lang":"en","type":"article","venue":"IEEE Transactions on Green Communications and Networking","topic":"Energy Harvesting in Wireless Networks","field":"Engineering","cited_by":46,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"Natural Science Foundation of Zhejiang Province; Natural Sciences and Engineering Research Council of Canada; National Natural Science Foundation of China","keywords":"Relay; Computer science; Scheduling (production processes); Power control; Throughput; Mathematical optimization; Online algorithm; Efficient energy use; Optimization problem; Relay channel; Channel state information; Wireless; Selection algorithm; Real-time computing; Power (physics); Selection (genetic algorithm); Engineering; Algorithm; Mathematics; Electrical engineering; Telecommunications","routes":{"ca_aff":true,"ca_fund":true,"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":[0.0007996149,0.0009030165,0.0009473871,0.0002440068,0.0003667278,0.0008783918,0.0007297947,0.0006170789,0.001194095],"category_scores_gemma":[0.001862148,0.0004206221,0.0004030713,0.0004597345,0.0007373081,0.001016689,0.0006539392,0.0007204806,0.0002550256],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007614891,"about_ca_system_score_gemma":0.00082464,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00181186,"about_ca_topic_score_gemma":0.001622083,"domain_scores_codex":[0.9993005,0.0002595017,0.00003157275,0.0001990603,0.0001139202,0.00009551772],"domain_scores_gemma":[0.999402,0.0003810122,0.00008709451,0.00004144893,0.00006717708,0.00002119998],"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":[0.000119252,0.00006520776,0.0003629474,0.0000972148,0.00002694177,0.0001544559,0.0001053342,0.9354355,0.008968018,0.01744861,0.001008966,0.03620744],"study_design_scores_gemma":[0.000008849661,0.0000292575,0.00005785747,0.000002876305,0.00000625997,0.00002277419,0.00001242916,0.9943972,0.001039722,0.004169027,0.0002491514,0.000004667831],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01926533,0.000448641,0.9779799,0.0001512992,0.00002769581,0.0000296941,0.00003095611,0.0001128455,0.001953518],"genre_scores_gemma":[0.9284958,0.0007941712,0.06832284,0.00006133915,0.00005237607,0.0000946674,0.00005291943,0.00003575388,0.002090088],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.00181186,"threshold_uncertainty_score":0.005525053,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01880251807547889,"score_gpt":0.2376646081727937,"score_spread":0.2188620900973148,"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."}}