{"id":"W3004198548","doi":"10.1109/tsp.2020.2970309","title":"Energy-Optimal Multiple Access Computation Offloading: Signalling Structure and Efficient Communication Resource Allocation","year":2020,"lang":"en","type":"article","venue":"IEEE Transactions on Signal Processing","topic":"IoT and Edge/Fog Computing","field":"Computer Science","cited_by":13,"is_retracted":false,"has_abstract":true,"ca_institutions":"McMaster University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Exploit; Resource allocation; Computation offloading; Time division multiple access; Signalling; Efficient energy use; Distributed computing; Energy consumption; Computation; Wireless; Computer network; Channel allocation schemes; Embedded system; Edge computing; Algorithm; Telecommunications; Internet of Things","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":[0.0004342468,0.000474353,0.000438809,0.0003136129,0.0003994552,0.0007613878,0.0006531075,0.0005332314,0.001632052],"category_scores_gemma":[0.001566691,0.0001748167,0.0002857737,0.0005104236,0.0006485161,0.0009618995,0.0006862656,0.0006798714,0.0002455815],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005500301,"about_ca_system_score_gemma":0.001272218,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001460353,"about_ca_topic_score_gemma":0.001956545,"domain_scores_codex":[0.9996428,0.0001063721,0.00001843262,0.00004790267,0.0001042528,0.0000800991],"domain_scores_gemma":[0.9995083,0.0002535646,0.00005145007,0.00006775431,0.00008593443,0.00003299324],"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":[0.0002222285,0.0001148941,0.0003912523,0.0001057893,0.00002386363,0.00009267867,0.000103877,0.8113561,0.03371041,0.05830754,0.001587193,0.09398411],"study_design_scores_gemma":[0.000009484078,0.00003414566,0.00007412009,0.000003326267,0.000002572802,0.00002231923,0.00001120257,0.9921147,0.001923042,0.005366885,0.0004321522,0.000006009756],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.04141125,0.0001833134,0.9533859,0.0001718674,0.00005028064,0.00004927362,0.00003033746,0.0001715592,0.004546243],"genre_scores_gemma":[0.8481587,0.0001327246,0.1494041,0.0000784806,0.00003282409,0.00008643098,0.00004651551,0.00002899236,0.00203123],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.001632052,"threshold_uncertainty_score":0.005459785,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03121447479925236,"score_gpt":0.2603558603003952,"score_spread":0.2291413855011428,"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."}}