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Social Cognitive Radio Networks 2015 ed. [Minkštas viršelis]

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  • Formatas: Paperback / softback, 83 pages, aukštis x plotis: 235x155 mm, weight: 1591 g, 32 Illustrations, black and white; XI, 83 p. 32 illus., 1 Paperback / softback
  • Serija: SpringerBriefs in Electrical and Computer Engineering
  • Išleidimo metai: 28-Jan-2015
  • Leidėjas: Springer International Publishing AG
  • ISBN-10: 3319152149
  • ISBN-13: 9783319152141
Kitos knygos pagal šią temą:
Social Cognitive Radio Networks 2015 ed.Didesnis paveikslėlis
  • Formatas: Paperback / softback, 83 pages, aukštis x plotis: 235x155 mm, weight: 1591 g, 32 Illustrations, black and white; XI, 83 p. 32 illus., 1 Paperback / softback
  • Serija: SpringerBriefs in Electrical and Computer Engineering
  • Išleidimo metai: 28-Jan-2015
  • Leidėjas: Springer International Publishing AG
  • ISBN-10: 3319152149
  • ISBN-13: 9783319152141
Kitos knygos pagal šią temą:
Pastovi nuoroda: https://www.kriso.lt/db/9783319152141.html
Raktažodžiai:
This brief presents research results on social cognitive radio networks, a transformational and innovative networking paradigm that promotes the nexus between social interactions and cognitive radio networks. Along with a review of the research literature, the text examines the key motivation and challenges of social cognitive radio network design. Three socially inspired distributed spectrum sharing mechanisms are introduced: adaptive channel recommendation mechanism, imitation-based social spectrum sharing mechanism, and evolutionarily stable spectrum access mechanism. The brief concludes with a discussion of future research directions which ascertains that exploiting social interactions for distributed spectrum sharing will advance the state-of-the-art of cognitive radio network design, spur a new line of thinking for future wireless networks, and enable novel wireless service and applications.
1 Overview
1(6)
1.1 Spectrum Under-Utilization Issue
1(1)
1.2 Social Cognitive Radio Networks
2(1)
1.3 Related Research
3(4)
References
4(3)
2 Adaptive Channel Recommendation Mechanism
7(28)
2.1 Introduction
7(2)
2.2 System Model
9(2)
2.3 Introduction to Channel Recommendation
11(3)
2.3.1 Review of Static Channel Recommendation
12(1)
2.3.2 Motivations for Adaptive Channel Recommendation
13(1)
2.4 Adaptive Channel Recommendation with Channel Homogeneity
14(3)
2.4.1 MDP Formulation for Adaptive Channel Recommendation
15(1)
2.4.2 Existence of Optimal Stationary Policy
16(1)
2.4.3 Structure of Optimal Stationary Policy
16(1)
2.5 Model Reference Adaptive Search for Optimal Spectrum Access Policy
17(5)
2.5.1 Model Reference Adaptive Search Method
18(1)
2.5.2 Model Reference Adaptive Search for Optimal Spectrum Access Policy
19(3)
2.5.3 Convergence of Model Reference Adaptive Search
22(1)
2.6 Adaptive Channel Recommendation with Channel Heterogeneity
22(2)
2.7 Adaptive Channel Recommendation in General Channel Environment
24(1)
2.8 Simulation Results
25(7)
2.8.1 Simulation Setup
26(2)
2.8.2 Heuristic Heterogenous Channel Recommendation
28(1)
2.8.3 Simulation with Real Channel Data
29(3)
2.9 Summary
32(3)
References
32(3)
3 Imitative Spectrum Access Mechanism
35(26)
3.1 Introduction
35(1)
3.2 System Model
36(4)
3.2.1 Spectrum Sharing System Model
36(3)
3.2.2 Social Information Sharing Graph
39(1)
3.3 Imitative Spectrum Access Mechanism
40(4)
3.3.1 Expected Throughput Estimation
40(3)
3.3.2 Imitative Spectrum Access
43(1)
3.4 Convergence of Imitative Spectrum Access
44(7)
3.4.1 Cluster-Based Graphical Representation of Information Sharing Graph
45(2)
3.4.2 Dynamics of Imitative Spectrum Access
47(2)
3.4.3 Convergence of Imitative Spectrum Access
49(2)
3.5 Imitative Spectrum Access with User Heterogeneity
51(1)
3.6 Simulation Results
52(7)
3.6.1 Imitative Spectrum Access with Homogeneous Users
53(3)
3.6.2 Imitative Spectrum Access with Heterogeneous Users
56(1)
3.6.3 Performance Comparison
57(2)
3.7 Summary
59(2)
References
59(2)
4 Evolutionarily Stable Spectrum Access Mechanism
61(22)
4.1 Introduction
61(1)
4.2 System Model
62(2)
4.3 Overview of Evolutionary Game Theory
64(2)
4.3.1 Replicator Dynamics
64(1)
4.3.2 Evolutionarily Stable Strategy
65(1)
4.4 Evolutionary Spectrum Access
66(3)
4.4.1 Evolutionary Game Formulation
66(1)
4.4.2 Evolutionary Dynamics
67(1)
4.4.3 Evolutionary Equilibrium in Asymptotic Case λmax = ∞
68(1)
4.4.4 Evolutionary Equilibrium in General Case λmax lt; ∞
69(1)
4.5 Learning Mechanism for Distributed Spectrum Access
69(4)
4.5.1 Learning Mechanism for Distributed Spectrum Access
70(2)
4.5.2 Convergence of Learning Mechanism
72(1)
4.6 Simulation Results
73(8)
4.6.1 Evolutionary Spectrum Access in Large User Population Case
73(4)
4.6.2 Distributed Learning Mechanism in Large User Population Case
77(1)
4.6.3 Evolutionary Spectrum Access and Distributed Learning in Small User Population Case
77(2)
4.6.4 Performance Comparison
79(2)
4.7 Summary
81(2)
References
81(2)
5 Conclusion
83