{"id":"W3195227469","doi":"10.1109/tcomm.2021.3106099","title":"Exploiting Impacts of Antenna Selection and Energy Harvesting for Massive Network Connectivity","year":2021,"lang":"en","type":"article","venue":"IEEE Transactions on Communications","topic":"Energy Harvesting in Wireless Networks","field":"Engineering","cited_by":34,"is_retracted":false,"has_abstract":true,"ca_institutions":"Memorial University of Newfoundland; Exfo Electro-Optical Engineering (Canada)","funders":"","keywords":"Computer science; Energy harvesting; Throughput; Interference (communication); Transmission (telecommunications); Stochastic geometry; Small cell; Antenna (radio); Selection (genetic algorithm); Cellular network; Noma; Energy (signal processing); Communications system; Electronic engineering; Efficient energy use; Computer network; Distributed computing; Wireless; Telecommunications; Telecommunications link; Engineering; Electrical engineering; Channel (broadcasting); Mathematics","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":[0.000351547,0.0003070588,0.000285838,0.0001517374,0.0002487468,0.0005113017,0.0003525146,0.0003149771,0.000878234],"category_scores_gemma":[0.001436156,0.000130609,0.0001976089,0.0003568003,0.0005004494,0.000646613,0.0004915604,0.0002917525,0.00009725414],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000314289,"about_ca_system_score_gemma":0.000249466,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0005466776,"about_ca_topic_score_gemma":0.00109276,"domain_scores_codex":[0.9998035,0.00008484799,0.000004160245,0.00002402825,0.00004932742,0.00003412466],"domain_scores_gemma":[0.9993014,0.0005322049,0.00006295731,0.0000426591,0.00003845631,0.00002229313],"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":[0.0001199833,0.00006901515,0.002728726,0.0001008621,0.00004469666,0.0007324606,0.0001311766,0.8836069,0.04216644,0.04025035,0.0007459717,0.02930352],"study_design_scores_gemma":[0.000004541451,0.00007180146,0.0007483364,0.000004909304,0.00001484165,0.0001468452,0.00004836639,0.9880605,0.002505327,0.007994562,0.000393158,0.000006790428],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3210858,0.000832035,0.6616147,0.0004463095,0.00006456858,0.0000411359,0.00005449377,0.0001703932,0.01569052],"genre_scores_gemma":[0.9936743,0.0002214869,0.005511645,0.00002284468,0.000008779431,0.00001002931,0.000007393932,0.0000055095,0.000537992],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.000878234,"threshold_uncertainty_score":0.002938032,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0251014766377111,"score_gpt":0.2444844166799369,"score_spread":0.2193829400422258,"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."}}