Atnaujinkite slapukų nuostatas

El. knyga: Next Generation Marine Wireless Communication Networks

  • Formatas: PDF+DRM
  • Serija: Wireless Networks
  • Išleidimo metai: 25-Apr-2022
  • Leidėjas: Springer Nature Switzerland AG
  • Kalba: eng
  • ISBN-13: 9783030973070
  • Formatas: PDF+DRM
  • Serija: Wireless Networks
  • Išleidimo metai: 25-Apr-2022
  • Leidėjas: Springer Nature Switzerland AG
  • Kalba: eng
  • ISBN-13: 9783030973070

DRM apribojimai

  • Kopijuoti:

    neleidžiama

  • Spausdinti:

    neleidžiama

  • El. knygos naudojimas:

    Skaitmeninių teisių valdymas (DRM)
    Leidykla pateikė šią knygą šifruota forma, o tai reiškia, kad norint ją atrakinti ir perskaityti reikia įdiegti nemokamą programinę įrangą. Norint skaityti šią el. knygą, turite susikurti Adobe ID . Daugiau informacijos  čia. El. knygą galima atsisiųsti į 6 įrenginius (vienas vartotojas su tuo pačiu Adobe ID).

    Reikalinga programinė įranga
    Norint skaityti šią el. knygą mobiliajame įrenginyje (telefone ar planšetiniame kompiuteryje), turite įdiegti šią nemokamą programėlę: PocketBook Reader (iOS / Android)

    Norint skaityti šią el. knygą asmeniniame arba „Mac“ kompiuteryje, Jums reikalinga  Adobe Digital Editions “ (tai nemokama programa, specialiai sukurta el. knygoms. Tai nėra tas pats, kas „Adobe Reader“, kurią tikriausiai jau turite savo kompiuteryje.)

    Negalite skaityti šios el. knygos naudodami „Amazon Kindle“.

This book presents a novel framework design for  the next generation Marine Wireless Communication Networks (MWCNs). The authors first provide an overview of MWCNs, followed by a discussion of challenges in the design and development of MWCNs in support of a diversity of marine services such as real-time marine monitoring, offshore oil exploration, drilling, marine tourism and fishing. The authors then propose cross layer networking solutions to achieve a high performance modern MWCN that enables efficient and reliable data transmissions under hostile marine environment, which include the network deployment, the physical layer channel coding, intelligent network access and resource management, and learning-based opportunistic routing. Finally, the authors summarize the book and present some open issues that will lead to new research directions in the next generation MWCNs.
1 Introduction
1(32)
1.1 Overview of Marine Wireless Communications and Networks (MWCNs)
3(17)
1.1.1 Maritime Applications in MWCNs
3(3)
1.1.2 Current Marine Wireless Communication Networks
6(11)
1.1.3 The Next Generation Marine Wireless Communication Networks
17(3)
1.2 Challenges
20(8)
1.2.1 Deployment Challenges
20(2)
1.2.2 Physical Layer Challenges
22(3)
1.2.3 Link Layer Challenges
25(2)
1.2.4 Network Layer Challenges
27(1)
1.3 Organization of the Book
28(5)
References
28(5)
2 Topology Optimization of MWCN
33(26)
2.1 Background
33(2)
2.2 Related Works
35(1)
2.3 Network Model and Problem Formulation
36(6)
2.3.1 Network Model
36(1)
2.3.2 Energy Model
37(1)
2.3.3 Problem Formulation
38(4)
2.4 Ant Colony Based Efficient Topology Optimization (AC-ETO)
42(4)
2.4.1 Algorithm Description
42(3)
2.4.2 Computational Complexity Analysis
45(1)
2.5 Simulations and Discussions
46(10)
2.5.1 Performance Validation in Small Scale to Middle Scale Networks
47(2)
2.5.2 Performance Analysis of Gurobi and AC-ETO in Different Network Scenarios
49(2)
2.5.3 Performance Comparison of AC-ETO and a Greedy Algorithm
51(5)
2.6 Conclusion
56(3)
References
56(3)
3 Autoencoder with Channel Estimation for Marine Communications
59(24)
3.1 Background
60(2)
3.2 Typical OFDM Communication Systems
62(2)
3.3 Proposed OFDM Autoencoder
64(7)
3.3.1 CNN-Based OFDM Autoencoder
64(2)
3.3.2 Coded CNN-Based OFDM Autoencoder Using LSTM
66(2)
3.3.3 CNN-Based Channel Estimation
68(2)
3.3.4 Model Training
70(1)
3.4 Simulation Results
71(7)
3.4.1 AWGN and Fading Channels
72(4)
3.4.2 Channel Estimation
76(2)
3.5 Conclusion
78(5)
References
80(3)
4 Decentralized Reinforcement Learning-Based Access Control for Energy Sustainable Underwater Acoustic Sub-Network of MWCN
83(24)
4.1 Background
84(2)
4.2 Related Works
86(2)
4.3 Performance Analysis of ESUN with Energy Harvesting
88(8)
4.3.1 System Model
88(2)
4.3.2 Analysis of ESUN Nodes
90(5)
4.3.3 Optimization Problem
95(1)
4.4 Learning-Based Random Access for ESUN Nodes
96(3)
4.5 Performance Evaluation
99(4)
4.6 Conclusions
103(4)
References
104(3)
5 Opportunistic Routing with Q-Learning for Marine Wireless Sensor Networks
107(32)
5.1 Background
108(3)
5.2 Related Works
111(4)
5.3 System Model
115(3)
5.3.1 Network Architecture
115(1)
5.3.2 Q-Learning Model
116(2)
5.4 EDORQ Algorithm
118(7)
5.4.1 Overview of EDORQ
118(1)
5.4.2 Void Detection Based Candidate Set Selection
119(2)
5.4.3 Q-Learning Based Candidate Set Coordination
121(4)
5.4.4 Summary
125(1)
5.5 Simulation Results and Analysis
125(10)
5.5.1 Simulation Setup
125(1)
5.5.2 Simulation Metrics
126(1)
5.5.3 Simulation Results
127(8)
5.6 Conclusions
135(4)
References
136(3)
6 Conclusions and Future Directions
139(6)
6.1 Conclusions
139(1)
6.2 Future Research Directions
140(5)
References
143(2)
Index 145
Bin Lin is a professor and Dean of Communication Engineering Department, School of Information Science and Technology, Dalian Maritime University. Her research interests include wireless communications, network dimensioning and optimization, resource allocation, artificial intelligence, maritime communication networks, edge/cloud computing, wireless sensor networks, and Internet of Things. She received the B.S. and M.S. degrees from Dalian Maritime University, Dalian, China, in 1999 and 2003 respectively, and the Ph.D. degree from the Broadband Communications Research Group, Department of Electrical and Computer Engineering, University of Waterloo, Waterloo, ON, Canada, in 2009. She has been a Visiting Scholar with George Washington University, Washington, DC, USA, from 2015 to 2016. She is the editor of IEEE TVT and IET Communications. She has authored/coauthored around 70 journal papers and 40 technical papers in conference proceedings. She served as a TPC member for IEEE Globecom, ICC, WCNC, and the technical reviewer for multiple IEEE Transactions including TMC, TVT, TWC, and ITS.Jianli Duan received the M.S. and Ph.D. degrees from Dalian Maritime University, Dalian, Liaoning Province, China, in 2003 and 2020, respectively. She is currently a teacher at Qingdao University of Technology, Qingdao, Shandong Province, China. Her research direction is maritime telecommunications and networking, wireless sensor networks, and network planning and optimization.







Mengqi Han received the B.S degree from the Department of Electronic and information, Nanjing University of Science and Technology, Nanjing, China, in 2013. And she received the M.S  and the Ph.D degree from the Department of Electrical and Computer Engineering in Illinois Institute of Technology, in 2015 and 2020 respectively. Her research interests include performance analysis of MAC protocol and protocol design for next-generation wireless networks, wireless networks resource management, reinforcement learning, and deep learning.





 





Lin X. Cai received the M.A.Sc. and Ph.D. degrees in Electrical and Computer Engineering from the University of Waterloo, Waterloo, Canada, in 2005 and 2010, respectively. She is currently an Associate Professor with the Department of Electrical and Computer Engineering, Illinois Institute of Technology, Chicago, Illinois, USA. Her research interests include green communication and networking, intelligent radio resource management, and wireless Internet of Things. She received a Postdoctoral Fellowship Award from the Natural Sciences and Engineering Research Council of Canada (NSERC) in 2010, a Best Paper Award from the IEEE Globecom 2011, an NSF Career Award in 2016, and the IIT Sigma Xi Research Award in the Junior Faculty Division in 2019. She is an Associate Editor of IEEE Transaction on Wireless Communications, IEEE Network Magazine, and a co-chair for IEEE conferences.