{"id":"W4286377390","doi":"10.1109/twc.2022.3190883","title":"Distributed Handoff Problem in Heterogeneous Networks With End-to-End Network Slicing: Decentralized Markov Decision Process-Based Modeling and Solution","year":2022,"lang":"en","type":"article","venue":"IEEE Transactions on Wireless Communications","topic":"Software-Defined Networks and 5G","field":"Computer Science","cited_by":13,"is_retracted":false,"has_abstract":true,"ca_institutions":"Carleton University","funders":"National Key Research and Development Program of China; Natural Science Foundation of Beijing Municipality; National Natural Science Foundation of China","keywords":"Handover; Computer science; Heterogeneous network; Markov decision process; Markov process; Computer network; Overhead (engineering); Partially observable Markov decision process; Distributed computing; Base station; Markov chain; Mathematical optimization; Wireless network; Markov model; Wireless; Telecommunications; 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":[0.001193492,0.0008036933,0.001414668,0.0004609731,0.0007005372,0.001062309,0.001453712,0.001508674,0.001450773],"category_scores_gemma":[0.001729236,0.0006678371,0.0009590166,0.0006058577,0.001041947,0.001013381,0.001399488,0.001470333,0.0001168591],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001803194,"about_ca_system_score_gemma":0.00263781,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.02180383,"about_ca_topic_score_gemma":0.01321607,"domain_scores_codex":[0.9992396,0.0002061619,0.00003128495,0.0001948313,0.0001372163,0.0001908773],"domain_scores_gemma":[0.998884,0.0006818923,0.0001610386,0.00003538419,0.0001438534,0.0000938599],"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":[0.00002260249,0.0000153729,0.000396386,0.00002124414,0.0000167281,0.0000713683,0.00002581842,0.9871066,0.0003044436,0.009485995,0.0002266875,0.002306843],"study_design_scores_gemma":[0.000004384573,0.000004418733,0.00004562185,0.000001322149,0.000003211853,0.000004108395,0.000005731451,0.9979985,0.00003473187,0.001847643,0.0000482409,0.000002040533],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02943802,0.0003596882,0.9667584,0.0005041239,0.00004998047,0.0000562742,0.0001127469,0.0001073964,0.002613389],"genre_scores_gemma":[0.9593029,0.0004864218,0.0366476,0.0001466637,0.00004728223,0.0001783599,0.0001449901,0.00002456223,0.003021284],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.02180383,"threshold_uncertainty_score":0.04335386,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01901757854113507,"score_gpt":0.2527879000594733,"score_spread":0.2337703215183382,"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."}}