{"id":"W2076579100","doi":"10.1109/tcomm.2015.2411266","title":"Cognitive and Energy Harvesting-Based D2D Communication in Cellular Networks: Stochastic Geometry Modeling and Analysis","year":2015,"lang":"en","type":"article","venue":"IEEE Transactions on Communications","topic":"Energy Harvesting in Wireless Networks","field":"Engineering","cited_by":347,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Manitoba","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Telecommunications link; Stochastic geometry; Cognitive radio; Computer science; Energy harvesting; Computer network; Interference (communication); Transmitter; Cellular network; Channel (broadcasting); Stochastic geometry models of wireless networks; Wireless; Signal-to-interference-plus-noise ratio; Electronic engineering; Energy (signal processing); Radio resource management; Telecommunications; Wireless network; Engineering; Power (physics); Mathematics; Physics","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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0003854297,0.0002218179,0.0002908836,0.0006236477,0.0002742294,0.00009111997,0.00037566,0.000157479,0.000003729142],"category_scores_gemma":[0.00002644237,0.0002709278,0.00005857941,0.001385231,0.0002015602,0.0002180317,0.00001246895,0.0005641647,0.000001441773],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001048606,"about_ca_system_score_gemma":0.00003323696,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0006075844,"about_ca_topic_score_gemma":0.003515066,"domain_scores_codex":[0.9987368,0.0002489643,0.0003917482,0.0002296202,0.0001448233,0.0002480447],"domain_scores_gemma":[0.9975955,0.001011931,0.00005972246,0.001030612,0.0001264952,0.0001757724],"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":[0.000009996875,0.00007672377,0.0001083957,0.000007677937,0.0001376254,5.651631e-7,0.0001907895,0.9919811,0.00001817293,0.0001771185,0.000003745737,0.007288011],"study_design_scores_gemma":[0.0005205836,0.00002476756,0.0001209025,0.000164685,0.0002911952,0.000002211806,0.0002030029,0.9982212,0.00006149484,0.0001215378,0.00001975784,0.0002486602],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.03670669,0.003214585,0.959128,0.000114677,0.00006301594,0.00008870488,0.00001877826,0.0002613373,0.0004042038],"genre_scores_gemma":[0.9920892,0.0007908717,0.006716105,0.00005548746,0.00001176969,0.0001585604,0.00008928398,0.00004776926,0.00004095615],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9553825,"threshold_uncertainty_score":0.9999743,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03234404328833789,"score_gpt":0.2432006864108595,"score_spread":0.2108566431225216,"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."}}