運用深度多模型數據融合之行動網路流量預測

以作者查詢圖書館館藏

、以作者查詢臺灣博碩士

、以作者查詢全國書目

、勘誤回報

、線上人數：48

、訪客IP：18.191.228.88

姓名

吳禮哲(Li-Che Wu) 查詢紙本館藏

畢業系所

通訊工程學系

論文名稱

運用深度多模型數據融合之行動網路流量預測
(Deep Multi-Modal Data Fusion for Mobile Traffic Forecasting)

相關論文

★ 基於馬賽克特性之低失真實體電路佈局保密技術	★ 多路徑傳輸控制協定下從無線區域網路到行動網路之無縫換手
★ 感知網路下具預算限制之異質性子頻段分配	★ 下行服務品質排程在多天線傳輸環境下的效能評估
★ 多路徑傳輸控制協定下之整合型壅塞及路徑控制	★ Opportunistic Scheduling for Multicast over Wireless Networks
★ 適用多用戶多輸出輸入系統之低複雜度比例公平性排程設計	★ 利用混合式天線分配之 LTE 異質網路 UE 與 MIMO 模式選擇
★ 基於有限預算標價式拍賣之異質性頻譜分配方法	★ 適用於 MTC 裝置 ID 共享情境之排程式分群方法
★ Efficient Two-Way Vertical Handover with Multipath TCP	★ 多路徑傳輸控制協定下可亂序傳輸之壅塞及排程控制
★ 移動網路下適用於閘道重置之群體換手機制	★ 使用率能小型基地台之拍賣是行動數據分流方法
★ 高速鐵路環境下之通道預測暨比例公平性排程設計	★ 用於行動網路效能評估之混合式物聯網流量產生器

檔案

[Endnote RIS 格式]

[Bibtex 格式]

[相關文章]

[文章引用]

[完整記錄]

[館藏目錄]

[檢視]

[下載]

本電子論文使用權限為同意立即開放。
已達開放權限電子全文僅授權使用者為學術研究之目的，進行個人非營利性質之檢索、閱讀、列印。
請遵守中華民國著作權法之相關規定，切勿任意重製、散佈、改作、轉貼、播送，以免觸法。

摘要(中)

隨著第五代（5G）和深度學習（DL）的快速發展，對數據傳輸容量的需求越來越大。基於大數據的分析和預測是智能地管理小區站點的趨勢。為了充分利用ML，強大的ML模型：3D CNN，RNN和CNN-RNN非常可靠。在我們之前的工作中，我們已經掌握了基於一種數據，互聯網流量預測移動互聯網流量的技術cite{previous_paper}。但是，互聯網流量不僅受到一個因素的影響。這可能是由於外部條件造成的，這反過來影響了我們想要預測的目標。在這項工作中，我們使用2013年11月和12月期間來自米蘭市的互聯網流量，定期數據，天氣，新聞和社交數據來捕捉多數據與深度多模態學習模型之間的關係，稱為Multi-Modal CNN-RNN（MMCR）比僅有一種數據帶來更精確的預測。通過融合方法組合不同的數據，以便相關數據可以幫助我們預測目標。我們還使用學習方法通過深度學習模型調整當前時間的數據。實驗結果表明，使用有助於網絡流量的數據可以提高預測精度。我們還與其他工作設計的架構進行了比較。我們的方法也可以得到很好的結果。

摘要(英)

With the fast development of the fifth-generation (5G) and deep learning (DL), the demand for data transmission capacity is getting more and more. Analyzing and forecasting based on big data is a tendency to manage cell sites intelligently. To have full use of ML, the powerful ML model: 3D CNN, RNN, and CNN-RNN are very reliable. We have already grasped the technique of predicting mobile internet traffic based on one kind of data, internet traffic, in our previous work cite{previous_paper}. However, internet traffic is not only affected by one factor. It may be due to external conditions, which in turn affects the goals we want to predict. In this work, we use internet traffic, periodic data, weather, news and social data from the city of Milan during November and December in 2013 to catch the relationship between multi-data with a deep multi-modal learning model, called Multi-modal CNN-RNN (MMCR), bring on more precise forecasting than only one kind of data. Combine different data through a fused approach, so that relevant data can help us to predict the target. We also use the learning method to adjust the data at the current time through the deep learning model. The experimental results show that using data that is helpful for network traffic can improve prediction accuracy. And we also compare with the architecture designed by other work. Our method can also get good results.

關鍵字(中)

★ 1.深度學習
★ 2.數據融合
★ 3.行動網路流量預測

關鍵字(英)

★ 1.Deep Learning
★ 2.Data Fusion
★ 3.Mobile Traffic Forecasting

論文目次

1 Introduction
1.1 Background.................................. 1
1.2 Motivation................................... 2
1.3 Contribution.................................. 3
1.4 Framework.................................. 4
2 BackgroundandRelatedWorks
2.1 Multi-ModalLearning............................ 5
2.1.1 NeuralNetwork............................ 5
2.1.2 Fusionstructure............................ 6
2.2 TelecomItaliaDataSet............................ 8
2.2.1 CallDetailRecords.......................... 8
2.2.2 SocialpulseandMilanoToday.................... 9
2.2.3 Weatherstationdata......................... 10
2.3 Datafusion.................................. 11
2.4 TrafficForecasting.............................. 12
3 DeepMulti-ModelDataFusion
3.1 ArchitectureOverview............................ 14
3.2 ProcessingGridbyGridData......................... 15
3.3 ProcessingLargeAreaData......................... 17
3.4 FeatureExtractionStage........................... 18
3.5 DataFusionandMulti-taskRegressionStage................ 19
4 ExperimentsandResult
4.1 ExperimentalSettings............................. 21
4.2 Cross-ModelPerformance.......................... 23
4.3 OverallPerformance............................. 26
5 ConclusionandFutureWork
5.1 Conclusion.................................. 34
5.2 FutureWork.................................. 34
Bibliography

參考文獻

[1] Pradeep K.Atrey,M.AnwarHossain,AbdulmotalebElSaddik,andMohanS.
Kankanhalli. Multimodalfusionformultimediaanalysis:asurvey. Multimedia Sys-
tems, 16(6):345–379,Nov2010.
[2] Gianni Barlacchi,MarcoDeNadai,RobertoLarcher,AntonioCasella,Cristiana
Chitic, GiovanniTorrisi,FabrizioAntonelli,AlessandroVespignani,AlexPentland,
and BrunoLepri.Amulti-sourcedatasetofurbanlifeinthecityofMilanandthe
ProvinceofTrentino. Scientific data, 2:150055,2015.
[3] Cisco VisualNetworkingIndexCisco.Globalmobiledatatrafficforecastupdate,
2016–2021. white paper, 2016.
[4] PauloCortez,MiguelRio,MiguelRocha,andPedroSousa.Multi-scaleInternet
trafficforecastingusingneuralnetworksandtimeseriesmethods. Expert Systems,
29(2):143–155, 2012.
[5] A. Damnjanovic,J.Montojo,YongbinWei,TingfangJi,TaoLuo,M.Vajapeyam,Tae-
sang Yoo,OsokSong,andD.Malladi.Asurveyon3GPPheterogeneousnetworks.
WirelessCommunications,IEEE, 18(3):10–21,June2011.
[6] DennyBritz.Recurrentneuralnetworkstutorial,part1–introductiontornns.
[7] JeffDonahue,LisaAnneHendricks,SergioGuadarrama,MarcusRohrbach,Sub-
hashini Venugopalan,KateSaenko,andTrevorDarrell.Long-termrecurrentconvo-
lutional networksforvisualrecognitionanddescription. CoRR, abs/1411.4389,2014.
[8] G.E. HintonandR.R.Salakhutdinov.Reducingthedimensionalityofdatawithneural
networks. Science (NewYork,N.Y.), 313:504–7,082006.
[9] C. Huang,C.Chiang,andQ.Li.Astudyofdeeplearningnetworksonmobiletraffic
forecasting. In 2017 IEEE28thAnnualInternationalSymposiumonPersonal,Indoor,
and MobileRadioCommunications(PIMRC), pages1–6,Oct2017.
[10] C. W.Huang,C.T.Chiang,andQ.Li.Astudyofdeeplearningnetworksonmobile
trafficforecasting.In 2017 IEEE28thAnnualInternationalSymposiumonPersonal,
Indoor,andMobileRadioCommunications(PIMRC), pages1–6,Oct2017.
[11] WenhaoHuang,GuojieSong,HaikunHong,andKunqingXie.Deeparchitecturefor
trafficflowprediction:Deepbeliefnetworkswithmultitasklearning. IEEE Transac-
tions onIntelligentTransportationSystems, 15(5):2191–2201,2014.
[12] WenweiJin,YoufangLin,ZhihaoWu,andHuaiyuWan.Spatio-temporalrecurrent
convolutionalnetworksforcitywideshort-termcrowdflowsprediction.In Proceed-
ings ofthe2NdInternationalConferenceonComputeandDataAnalysis, ICCDA
2018, pages28–35,NewYork,NY,USA,2018.ACM.
[13] Mingyu KimJuhanNamHonglakLeeAndrewY.NgJiquanNgiam,AdityaKhosla.
Multimodal deeplearning. Proc.28thInt.Conf.MachineLearning(ICML-11), 2011.
[14] Bahador Khaleghi,AlaaM.Khamis,FakhriKarray,andSaiedehN.Razavi.Multi-
sensor datafusion:Areviewofthestate-of-the-art. Information Fusion, 14:28–44,
2013.
[15] D. Lahat,T.Adali,andC.Jutten.Multimodaldatafusion:Anoverviewofmethods,
challenges, andprospects. ProceedingsoftheIEEE, 103(9):1449–1477,Sept2015.
[16] Y.Lecun,L.Bottou,Y.Bengio,andP.Haffner.Gradient-basedlearningappliedto
document recognition. ProceedingsoftheIEEE, 86(11):2278–2324,Nov1998.
[17] YannLeCun,BernhardEBoser,JohnSDenker,DonnieHenderson,RichardE
Howard,WayneEHubbard,andLawrenceDJackel.Handwrittendigitrecogni-
tion withaback-propagationnetwork.In Advances inneuralinformationprocessing
systems, pages396–404,1990.
[18] LISA lab.Convolutionalneuralnetworks(lenet).
[19] Jonathan Masci,MichaelM.Bronstein,AlexanderM.Bronstein,andJ¨urgenSchmid-
huber.Multimodalsimilarity-preservinghashing. CoRR, abs/1207.1522,2012.
[20] TiagoPradoOliveira,JamilSalemBarbar,andAlexsandroSantosSoares.Computer
networktrafficprediction:acomparisonbetweentraditionalanddeeplearningneural
networks. International JournalofBigDataIntelligence, 3(1):28–37,2016.
[21] SoujanyaPoria,ErikCambria,andAlexanderF.Gelbukh.Deepconvolutionalneural
networktextualfeaturesandmultiplekernellearningforutterance-levelmultimodal
sentiment analysis.In EMNLP, 2015.
[22] D. RamachandramandG.W.Taylor.Deepmultimodallearning:Asurveyonrecent
advancesandtrends. IEEE SignalProcessingMagazine, 34(6):96–108,Nov2017.
[23] Nitish SrivastavaandRuslanSalakhutdinov.Multimodallearningwithdeepboltz-
mann machines. JournalofMachineLearningResearch, 15:2949–2980,2014.
[24] D. Wang,P.Cui,M.Ou,andW.Zhu.Learningcompacthashcodesformultimodal
representations usingorthogonaldeepstructure. IEEE TransactionsonMultimedia,
17(9):1404–1416, Sep.2015.
[25] D. Wu,L.Pigou,P.Kindermans,N.D.Le,L.Shao,J.Dambre,andJ.Odobez.
Deep dynamicneuralnetworksformultimodalgesturesegmentationandrecognition.
IEEE TransactionsonPatternAnalysisandMachineIntelligence, 38(8):1583–1597,
Aug 2016.
[26] D. Wu,L.Pigou,P.J.Kindermans,N.D.H.Le,L.Shao,J.Dambre,andJ.M.
Odobez. Deepdynamicneuralnetworksformultimodalgesturesegmentationand
recognition. IEEE TransactionsonPatternAnalysisandMachineIntelligence,
38(8):1583–1597, Aug2016.
[27] Jing Xu,JiangWang,YuanpingZhu,YangYang,XiaojinZheng,ShuangdieWang,
LigangLiu,KariHorneman,andYongTeng.Cooperativedistributedoptimizationfor
the hyper-densesmallcelldeployment. IEEE CommunicationsMagazine, 52(5):61–
67, 2014.
[28] C. Zhang,H.Zhang,D.Yuan,andM.Zhang.Citywidecellulartrafficprediction
based ondenselyconnectedconvolutionalneuralnetworks. IEEE Communications
Letters, 22(8):1656–1659,Aug2018.
[29] Junbo Zhang,YuZheng,andDekangQi.Deepspatio-temporalresidualnetworksfor
citywide crowdflowsprediction.In ProceedingsoftheThirty-FirstAAAIConference
on ArtificialIntelligence(AAAI-17), pages1655–1661,2017.
38

指導教授

黃志煒

審核日期

2019-8-20

推文