{"id":"https://openalex.org/W4308089117","doi":"https://doi.org/10.1109/secon55815.2022.9918560","title":"A Hybrid Deep Reinforcement Learning Approach for Dynamic Task Offloading in NOMA-MEC System","display_name":"A Hybrid Deep Reinforcement Learning Approach for Dynamic Task Offloading in NOMA-MEC System","publication_year":2022,"publication_date":"2022-09-20","ids":{"openalex":"https://openalex.org/W4308089117","doi":"https://doi.org/10.1109/secon55815.2022.9918560"},"language":"en","primary_location":{"id":"doi:10.1109/secon55815.2022.9918560","is_oa":false,"landing_page_url":"https://doi.org/10.1109/secon55815.2022.9918560","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2022 19th Annual IEEE International Conference on Sensing, Communication, and Networking (SECON)","raw_type":"proceedings-article"},"type":"article","indexed_in":["crossref"],"open_access":{"is_oa":false,"oa_status":"closed","oa_url":null,"any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5047831366","display_name":"Ce Shang","orcid":"https://orcid.org/0009-0004-3002-4107"},"institutions":[{"id":"https://openalex.org/I139759216","display_name":"Beijing University of Posts and Telecommunications","ror":"https://ror.org/04w9fbh59","country_code":"CN","type":"education","lineage":["https://openalex.org/I139759216"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Ce Shang","raw_affiliation_strings":["Beijing University of Posts and Telecommunications,Department of Computer Science","Department of Computer Science, Beijing University of Posts and Telecommunications"],"affiliations":[{"raw_affiliation_string":"Beijing University of Posts and Telecommunications,Department of Computer Science","institution_ids":["https://openalex.org/I139759216"]},{"raw_affiliation_string":"Department of Computer Science, Beijing University of Posts and Telecommunications","institution_ids":["https://openalex.org/I139759216"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5101997375","display_name":"Yan Sun","orcid":"https://orcid.org/0000-0001-5556-6739"},"institutions":[{"id":"https://openalex.org/I139759216","display_name":"Beijing University of Posts and Telecommunications","ror":"https://ror.org/04w9fbh59","country_code":"CN","type":"education","lineage":["https://openalex.org/I139759216"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yan Sun","raw_affiliation_strings":["Beijing University of Posts and Telecommunications,Department of Computer Science","Department of Computer Science, Beijing University of Posts and Telecommunications"],"affiliations":[{"raw_affiliation_string":"Beijing University of Posts and Telecommunications,Department of Computer Science","institution_ids":["https://openalex.org/I139759216"]},{"raw_affiliation_string":"Department of Computer Science, Beijing University of Posts and Telecommunications","institution_ids":["https://openalex.org/I139759216"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5010337399","display_name":"Hong Luo","orcid":"https://orcid.org/0000-0002-0672-3440"},"institutions":[{"id":"https://openalex.org/I139759216","display_name":"Beijing University of Posts and Telecommunications","ror":"https://ror.org/04w9fbh59","country_code":"CN","type":"education","lineage":["https://openalex.org/I139759216"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Hong Luo","raw_affiliation_strings":["Beijing University of Posts and Telecommunications,Department of Computer Science","Department of Computer Science, Beijing University of Posts and Telecommunications"],"affiliations":[{"raw_affiliation_string":"Beijing University of Posts and Telecommunications,Department of Computer Science","institution_ids":["https://openalex.org/I139759216"]},{"raw_affiliation_string":"Department of Computer Science, Beijing University of Posts and Telecommunications","institution_ids":["https://openalex.org/I139759216"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5047831366"],"corresponding_institution_ids":["https://openalex.org/I139759216"],"apc_list":null,"apc_paid":null,"fwci":1.7947,"has_fulltext":false,"cited_by_count":13,"citation_normalized_percentile":{"value":0.85613017,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":94,"max":98},"biblio":{"volume":null,"issue":null,"first_page":"434","last_page":"442"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10273","display_name":"IoT and Edge/Fog Computing","score":0.9997000098228455,"subfield":{"id":"https://openalex.org/subfields/1705","display_name":"Computer Networks and Communications"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},"topics":[{"id":"https://openalex.org/T10273","display_name":"IoT and Edge/Fog Computing","score":0.9997000098228455,"subfield":{"id":"https://openalex.org/subfields/1705","display_name":"Computer Networks and Communications"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T10502","display_name":"Advanced Memory and Neural Computing","score":0.9926000237464905,"subfield":{"id":"https://openalex.org/subfields/2208","display_name":"Electrical and Electronic Engineering"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T11458","display_name":"Advanced Wireless Communication Technologies","score":0.9922000169754028,"subfield":{"id":"https://openalex.org/subfields/2208","display_name":"Electrical and Electronic Engineering"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.8186758756637573},{"id":"https://openalex.org/keywords/reinforcement-learning","display_name":"Reinforcement learning","score":0.8081504702568054},{"id":"https://openalex.org/keywords/mobile-edge-computing","display_name":"Mobile edge computing","score":0.7475113868713379},{"id":"https://openalex.org/keywords/computation-offloading","display_name":"Computation offloading","score":0.6602059602737427},{"id":"https://openalex.org/keywords/distributed-computing","display_name":"Distributed computing","score":0.5582385659217834},{"id":"https://openalex.org/keywords/server","display_name":"Server","score":0.5537060499191284},{"id":"https://openalex.org/keywords/edge-computing","display_name":"Edge computing","score":0.45364782214164734},{"id":"https://openalex.org/keywords/enhanced-data-rates-for-gsm-evolution","display_name":"Enhanced Data Rates for GSM Evolution","score":0.42710429430007935},{"id":"https://openalex.org/keywords/computer-network","display_name":"Computer network","score":0.3792805075645447},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.23604026436805725}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8186758756637573},{"id":"https://openalex.org/C97541855","wikidata":"https://www.wikidata.org/wiki/Q830687","display_name":"Reinforcement learning","level":2,"score":0.8081504702568054},{"id":"https://openalex.org/C2776061582","wikidata":"https://www.wikidata.org/wiki/Q25325231","display_name":"Mobile edge computing","level":3,"score":0.7475113868713379},{"id":"https://openalex.org/C2781041963","wikidata":"https://www.wikidata.org/wiki/Q18348618","display_name":"Computation offloading","level":4,"score":0.6602059602737427},{"id":"https://openalex.org/C120314980","wikidata":"https://www.wikidata.org/wiki/Q180634","display_name":"Distributed computing","level":1,"score":0.5582385659217834},{"id":"https://openalex.org/C93996380","wikidata":"https://www.wikidata.org/wiki/Q44127","display_name":"Server","level":2,"score":0.5537060499191284},{"id":"https://openalex.org/C2778456923","wikidata":"https://www.wikidata.org/wiki/Q5337692","display_name":"Edge computing","level":3,"score":0.45364782214164734},{"id":"https://openalex.org/C162307627","wikidata":"https://www.wikidata.org/wiki/Q204833","display_name":"Enhanced Data Rates for GSM Evolution","level":2,"score":0.42710429430007935},{"id":"https://openalex.org/C31258907","wikidata":"https://www.wikidata.org/wiki/Q1301371","display_name":"Computer network","level":1,"score":0.3792805075645447},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.23604026436805725}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/secon55815.2022.9918560","is_oa":false,"landing_page_url":"https://doi.org/10.1109/secon55815.2022.9918560","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2022 19th Annual IEEE International Conference on Sensing, Communication, and Networking (SECON)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"score":0.8999999761581421,"display_name":"Affordable and clean energy","id":"https://metadata.un.org/sdg/7"}],"awards":[{"id":"https://openalex.org/G1334434090","display_name":null,"funder_award_id":"62172051,61877005","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"}],"funders":[{"id":"https://openalex.org/F4320321001","display_name":"National Natural Science Foundation of China","ror":"https://ror.org/01h0zpd94"}],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":40,"referenced_works":["https://openalex.org/W2160305061","https://openalex.org/W2772819097","https://openalex.org/W2777180355","https://openalex.org/W2791432311","https://openalex.org/W2792526074","https://openalex.org/W2792758175","https://openalex.org/W2800755725","https://openalex.org/W2905026759","https://openalex.org/W2921018545","https://openalex.org/W2959276766","https://openalex.org/W2963789838","https://openalex.org/W2964098968","https://openalex.org/W2966171167","https://openalex.org/W3003541603","https://openalex.org/W3004116256","https://openalex.org/W3004968144","https://openalex.org/W3036160090","https://openalex.org/W3036299867","https://openalex.org/W3038782910","https://openalex.org/W3040779716","https://openalex.org/W3041261812","https://openalex.org/W3042446967","https://openalex.org/W3043890719","https://openalex.org/W3047488480","https://openalex.org/W3082278499","https://openalex.org/W3105923694","https://openalex.org/W3117089396","https://openalex.org/W3117278457","https://openalex.org/W3118929245","https://openalex.org/W3131641277","https://openalex.org/W3147579435","https://openalex.org/W3161037238","https://openalex.org/W3173239515","https://openalex.org/W3173875532","https://openalex.org/W3178010443","https://openalex.org/W3191717679","https://openalex.org/W4205661627","https://openalex.org/W6692846177","https://openalex.org/W6797068371","https://openalex.org/W6799973354"],"related_works":["https://openalex.org/W4200420173","https://openalex.org/W3120617837","https://openalex.org/W3127808443","https://openalex.org/W2916011811","https://openalex.org/W3034137700","https://openalex.org/W4362496467","https://openalex.org/W2896883851","https://openalex.org/W2917127270","https://openalex.org/W2902693277","https://openalex.org/W4378977105"],"abstract_inverted_index":{"Mobile":[0],"edge":[1,24,88,112],"computing":[2,14],"(MEC)":[3],"has":[4],"been":[5],"regarded":[6],"as":[7],"a":[8,31,65,81,103,124,169,240],"promising":[9],"paradigm":[10],"for":[11,90,212],"increasing":[12],"the":[13,43,50,60,72,84,87,94,120,151,177,202,207,213,224,235],"capacity":[15],"of":[16,53,83,163],"mobile":[17],"devices":[18],"(MDs)":[19],"by":[20,49],"offloading":[21,63,74,116,195],"tasks":[22],"to":[23,40,86,109,143,172,229],"servers.":[25],"Non-orthogonal":[26],"multiple":[27,33],"access":[28,34],"(NOMA)":[29],"is":[30,129,210,227],"critical":[32],"technique":[35],"that":[36,77,223,233],"allows":[37],"many":[38],"MDs":[39,78],"transmit":[41],"on":[42,185],"same":[44],"resource":[45],"block":[46],"simultaneously.":[47],"Attracted":[48],"enormous":[51],"benefits":[52],"combining":[54],"NOMA":[55],"and":[56,98,115,168,180,194,206,215],"MEC,":[57],"we":[58,101],"investigate":[59],"dynamic":[61],"computation":[62,96],"in":[64],"multi-device":[66],"multi-server":[67],"NOMA-MEC":[68],"system.":[69],"We":[70,149],"consider":[71],"partial":[73],"policy":[75],"such":[76],"can":[79],"offload":[80],"portion":[82],"task":[85,117],"server":[89,113,192],"execution.":[91],"To":[92],"minimize":[93],"overall":[95],"delay":[97],"energy":[99],"consumption,":[100],"formulate":[102],"mixed":[104],"integer":[105],"programming":[106],"(MIP)":[107],"problem":[108,122],"jointly":[110],"optimize":[111],"selection":[114,193],"ratio.":[118],"Solving":[119],"optimization":[121],"with":[123],"discrete-continuous":[125],"hybrid":[126,152,236],"action":[127,147,237,243],"space":[128,238],"not":[130],"straightforward":[131],"since":[132],"most":[133],"existing":[134],"deep":[135,186],"reinforcement":[136],"learning":[137],"(DRL)":[138],"algorithms":[139,232],"are":[140],"only":[141],"applicable":[142],"discrete":[144,178,214],"or":[145],"continuous":[146,181,216],"spaces.":[148],"present":[150],"advantage":[153,208],"actor-critic":[154,160],"(HA2C)":[155],"approach,":[156],"which":[157],"employs":[158],"an":[159],"architecture":[161],"consisting":[162],"two":[164],"par-allel":[165],"actor":[166,179,182],"networks":[167,183,188],"critic":[170,199],"network,":[171],"tackle":[173],"this":[174],"problem.":[175],"Specifically,":[176],"based":[184],"neural":[187],"(DNNs)":[189],"determine":[190],"MEC":[191],"ratio,":[196],"respectively.":[197],"The":[198],"network":[200,217],"evaluates":[201],"current":[203],"state":[204],"value,":[205],"function":[209],"computed":[211],"parameter":[218],"updates.":[219],"Experimental":[220],"results":[221],"show":[222],"proposed":[225],"algorithm":[226],"superior":[228],"conventional":[230],"DRL":[231],"convert":[234],"into":[239],"unified":[241],"homogeneous":[242],"space.":[244]},"counts_by_year":[{"year":2025,"cited_by_count":5},{"year":2024,"cited_by_count":6},{"year":2023,"cited_by_count":2}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}