Atjaunināt sīkdatņu piekrišanu

E-grāmata: Energy and Spectrum Efficient Wireless Network Design

5.00/5 (6 ratings by Goodreads)
(Royal Institute of Technology, Stockholm),
  • Formāts: PDF+DRM
  • Izdošanas datums: 27-Nov-2014
  • Izdevniecība: Cambridge University Press
  • Valoda: eng
  • ISBN-13: 9781316190999
Citas grāmatas par šo tēmu:
  • Formāts - PDF+DRM
  • Cena: 57,09 €*
  • * ši ir gala cena, t.i., netiek piemērotas nekādas papildus atlaides
  • Ielikt grozā
  • Pievienot vēlmju sarakstam
  • Šī e-grāmata paredzēta tikai personīgai lietošanai. E-grāmatas nav iespējams atgriezt un nauda par iegādātajām e-grāmatām netiek atmaksāta.
Energy and Spectrum Efficient Wireless Network DesignPalielināt
  • Formāts: PDF+DRM
  • Izdošanas datums: 27-Nov-2014
  • Izdevniecība: Cambridge University Press
  • Valoda: eng
  • ISBN-13: 9781316190999
Citas grāmatas par šo tēmu:

DRM restrictions

  • Kopēšana (kopēt/ievietot):

    nav atļauts

  • Drukāšana:

    nav atļauts

  • Lietošana:

    Digitālo tiesību pārvaldība (Digital Rights Management (DRM))
    Izdevējs ir piegādājis šo grāmatu šifrētā veidā, kas nozīmē, ka jums ir jāinstalē bezmaksas programmatūra, lai to atbloķētu un lasītu. Lai lasītu šo e-grāmatu, jums ir jāizveido Adobe ID. Vairāk informācijas šeit. E-grāmatu var lasīt un lejupielādēt līdz 6 ierīcēm (vienam lietotājam ar vienu un to pašu Adobe ID).

    Nepieciešamā programmatūra
    Lai lasītu šo e-grāmatu mobilajā ierīcē (tālrunī vai planšetdatorā), jums būs jāinstalē šī bezmaksas lietotne: PocketBook Reader (iOS / Android)

    Lai lejupielādētu un lasītu šo e-grāmatu datorā vai Mac datorā, jums ir nepieciešamid Adobe Digital Editions (šī ir bezmaksas lietotne, kas īpaši izstrādāta e-grāmatām. Tā nav tas pats, kas Adobe Reader, kas, iespējams, jau ir jūsu datorā.)

    Jūs nevarat lasīt šo e-grāmatu, izmantojot Amazon Kindle.

"Covering the fundamental principles and state-of-the-art cross-layer techniques, this practical guide provides the tools needed to design MIMO- and OFDM-based wireless networks that are both energy- and spectrum-efficient. Technologies are introduced inparallel for both centralized and distributed wireless networks to give you a clear understanding of the similarities and differences between their energy- and spectrum-efficient designs, which is essential for achieving the highest network energy savingwithout losing performance. Cutting-edge green cellular network design technologies, enabling you to master resource management for next-generation wireless networks based on MIMO and OFDM, and detailed real-world implementation examples are provided to guide your engineering design in both theory and practice. Whether you are a graduate student, a researcher or a practitioner in industry, this is an invaluable guide"--

Papildus informācija

Provides the fundamental principles and practical tools needed to design next-generation wireless networks that are both energy- and spectrum-efficient.
Preface xv
Acronyms xvii
1 Introduction
1(10)
1.1 Motivation
1(1)
1.2 Wireless networks
2(6)
1.2.1 Overview
2(2)
1.2.2 Traditional layered architecture
4(2)
1.2.3 Necessity of cross-layer optimization
6(2)
1.3 Book outline
8(3)
Part I Basic concepts 11(42)
2 Wireless channel properties
15(9)
2.1 Path loss
15(1)
2.2 Shadowing
16(1)
2.3 Small-scale fading
17(3)
2.3.1 Flat-fading channels
18(2)
2.3.2 Frequency-selective fading channels
20(1)
2.4 Channel estimation
20(3)
2.4.1 Flat slow-fading channels
21(1)
2.4.2 Frequency-selective slow-fading channels
22(1)
2.4.3 Fast-fading channels
23(1)
2.4.4 Conclusion
23(1)
2.5 Other challenges
23(1)
3 Spectral and energy efficiency of wireless networks
24(7)
3.1 Spectral efficiency
24(1)
3.2 Energy efficiency
25(1)
3.3 Link metrics versus network metrics
26(5)
3.3.1 Link spectral efficiency
26(1)
3.3.2 Network spectral efficiency
27(2)
3.3.3 Link energy efficiency
29(1)
3.3.4 Network energy efficiency
29(2)
4 Centralized resource management in wireless networks
31(12)
4.1 Overview
31(1)
4.2 Wireless scheduling challenges
32(2)
4.3 Centralized scheduling algorithms
34(9)
4.3.1 Round-robin scheduling
35(1)
4.3.2 Max throughput scheduling
36(1)
4.3.3 Proportional fair scheduling
37(1)
4.3.4 Max-min scheduling
38(1)
4.3.5 Max utility scheduling
39(4)
5 Distributed resource management in wireless networks
43(10)
5.1 Overview
43(2)
5.2 Aloha
45(1)
5.2.1 Pure Aloha
45(1)
5.2.2 Slotted Aloha
46(1)
5.3 Carrier sense multiple access (CSMA)
46(2)
5.3.1 Non-persistent CSMA
47(1)
5.3.2 1-persistent CSMA
47(1)
5.3.3 p-persistent CSMA
47(1)
5.3.4 Effect of detection delay
47(1)
5.4 CSMA with collision detection
48(1)
5.5 Carrier sense multiple access with collision avoidance (CSMA/CA)
49(6)
5.5.1 Hidden and exposed terminal problems
49(1)
5.5.2 CSMA/CA protocol
50(3)
Part II Centralized cross-layer optimization 53(94)
6 Overview
55(6)
6.1 System model and problem description
56(3)
6.1.1 Channel characteristics in OFDM
56(2)
6.1.2 Rate adaptation in OFDM
58(1)
6.1.3 Dynamic subcarrier assignment and adaptive power allocation
58(1)
6.1.4 Queue structure
59(1)
6.1.5 Problem description
59(1)
6.2 Approach
59(2)
7 Utility-based optimization framework for OFDMA
61(11)
7.1 Rate-based utility functions
61(1)
7.2 Theoretical framework
62(10)
7.2.1 Problem formulation
62(1)
7.2.2 Dynamic subcarrier assignment
63(3)
7.2.3 Adaptive power allocation
66(3)
7.2.4 Properties of cross-layer optimization
69(3)
8 Algorithm development for utility-based optimization
72(22)
8.1 Dynamic subcarrier assignment (DSA) algorithms
72(5)
8.1.1 Optimality conditions
73(2)
8.1.2 Sorting-search algorithm of subcarrier assignment
75(2)
8.2 Adaptive power allocation (APA) algorithms
77(3)
8.2.1 APA for fixed subcarrier assignment
77(1)
8.2.2 Sequential-linear-approximation water-filling algorithm for continuous rate adaptation
78(1)
8.2.3 Greedy power allocation algorithm based on maximizing total utility for discrete rate adaptation
78(2)
8.3 Joint dynamic subcarrier assignment and adaptive power allocation
80(1)
8.4 Algorithm modification for non-concave utility functions
81(1)
8.5 Maximum utility with respect to average data rates
81(3)
8.6 Efficiency and fairness
84(3)
8.6.1 Fairness of "extreme OFDM" using utility functions with respect to instantaneous data rates
85(1)
8.6.2 Fairness of "practical OFDM" using utility functions with respect to average data rates
85(2)
8.7 Simulation results
87(6)
8.8 Summary
93(1)
9 Joint channel- and queue-aware multi-carrier scheduling using delay-based utility functions
94(23)
9.1 Introduction
94(1)
9.2 Extending scheduling rules in single-carrier networks into OFDMA networks
95(2)
9.2.1 Max-sum-capacity (MSC) rule
95(1)
9.2.2 Proportional fair (PF) scheduling
96(1)
9.2.3 Modified largest weighted delay first (M-LWDF) rule
96(1)
9.2.4 Exponential (EXP) rule
96(1)
9.3 Max-delay-utility (MDU) scheduling
97(3)
9.3.1 Utility functions
97(1)
9.3.2 Optimization objective
97(2)
9.3.3 Problem formulation in OFDMA
99(1)
9.3.4 Algorithms
100(1)
9.4 Stability
100(6)
9.4.1 Background and definition of stability
100(1)
9.4.2 Capacity region
101(1)
9.4.3 Maximum stability region
102(4)
9.5 Proof of Theorem 9.4
106(4)
9.6 Further improvement through delay transmit diversity and adaptive power allocation
110(2)
9.6.1 Joint dynamic subcarrier assignment and adaptive power allocation
110(1)
9.6.2 Delay transmit diversity
111(1)
9.7 Simulation results and performance comparison
112(4)
9.7.1 Performance comparison
112(4)
9.7.2 Improvement in delay transmit diversity and adaptive power allocation
116(1)
9.8 Summary
116(1)
10 Utility-based generalized QoS scheduling for heterogeneous traffic
117(9)
10.1 Introduction
117(1)
10.2 MDU scheduling for heterogeneous traffic
118(2)
10.2.1 Mechanisms of MDU scheduling for diverse QoS requirements
118(1)
10.2.2 Marginal utility functions for MDU scheduling
119(1)
10.3 Simulation
120(5)
10.3.1 Simulation conditions
120(1)
10.3.2 Simulation results
121(4)
10.4 Summary
125(1)
11 Asymptotic performance analysis for channel-aware scheduling
126(21)
11.1 Extreme value theory
126(3)
11.2 Asymptotic throughput analysis of single-carrier networks
129(10)
11.2.1 System model
129(1)
11.2.2 Throughput analysis for Rayleigh fading
130(3)
11.2.3 Throughput analysis for general channel distributions
133(3)
11.2.4 Throughput analysis for normalized-SNR-based scheduling
136(2)
11.2.5 Numerical results
138(1)
11.3 Asymptotic delay analysis of single-carrier networks
139(3)
11.3.1 Asymptotic distribution of service time
140(1)
11.3.2 Average waiting time
141(1)
11.4 Asymptotic performance analysis of multi-carrier networks
142(4)
11.4.1 Asymptotic throughput analysis
142(1)
11.4.2 Asymptotic delay analysis
143(1)
11.4.3 Delay performance comparison
144(2)
11.5 Summary
146(1)
Part III Distributed cross-layer optimization 147(88)
12 Overview
149(5)
12.1 Design objective
149(1)
12.2 Distributed multi-user diversity
150(1)
12.3 Approaches
151(3)
13 Opportunistic random access: single-cell cellular networks
154(10)
13.1 Channel-aware Aloha
154(6)
13.1.1 Protocol design and parameter optimization
157(2)
13.1.2 Performance analysis
159(1)
13.2 Opportunistic splitting algorithms
160(4)
14 Opportunistic random access: any network topology
164(18)
14.1 Network model
164(2)
14.2 Optimal design rules
166(4)
14.2.1 MAC layer analysis
167(1)
14.2.2 Physical layer analysis
168(1)
14.2.3 Criterion for cross-layer design
169(1)
14.3 Low-complexity MAC
170(3)
14.4 Optimal PHY operation
173(5)
14.4.1 Physical layer optimization with channel inversion
173(2)
14.4.2 Physical layer optimization with adaptive modulation and power allocation
175(3)
14.5 System performance
178(4)
14.5.1 Network performance improvement
178(2)
14.5.2 Suboptimality gap
180(2)
15 Optimal channel-aware distributed MAC
182(21)
15.1 System description
183(3)
15.2 Channel-aware medium access control
186(4)
15.3 Optimization
190(5)
15.3.1 CRS 1
191(1)
15.3.2 CRS k, k> 1
192(3)
15.4 Robustness analysis
195(3)
15.5 Simulation results
198(5)
16 Opportunistic random access with intelligent interference avoidance
203(14)
16.1 Intelligent interferer recognition
204(2)
16.2 Co-channel interference avoidance MAC
206(2)
16.3 Parameter optimization
208(3)
16.3.1 Trigger selection
208(2)
16.3.2 An alternate trigger mechanism using location knowledge
210(1)
16.4 Network performance
211(6)
16.4.1 Relationship of trigger and SNR
212(1)
16.4.2 Performance improvement
213(4)
17 Distributed power control
217(18)
17.1 System model
217(1)
17.2 Power control for real-time traffic
218(3)
17.2.1 Distributed power control
220(1)
17.3 Power control for elastic traffic
221(16)
17.3.1 Existence of equilibrium
224(1)
17.3.2 Uniqueness of equilibrium in single-channel systems
225(3)
17.3.3 Uniqueness of equilibrium in multi-channel systems
228(3)
17.3.4 Distributed power control with pricing
231(4)
Part IV Cross-layer optimization for energy-efficient networks 235(103)
18 Overview
237(7)
18.1 Lighting analogy
238(2)
18.2 Methodology
240(4)
19 Energy-efficient transmission
244(38)
19.1 Energy efficiency capacity
244(1)
19.2 Ideal transmission
245(1)
19.3 Energy-efficient transmission in practice
246(4)
19.4 Energy-efficient link adaptation in frequency-selective channels
250(13)
19.4.1 Modeling of energy-efficient link adaptation
252(1)
19.4.2 Design principles
253(3)
19.4.3 Constrained energy-efficient link adaptation
256(1)
19.4.4 Energy-efficient downlink OFDMA transmission
257(1)
19.4.5 Iterative algorithm design
258(4)
19.4.6 Energy efficiency gain
262(1)
19.5 Low-complexity energy-efficient link adaptation
263(3)
19.6 Energy-efficient MIMO and MU-MIMO link adaptation
266(16)
19.6.1 Energy-efficient MU-MIMO modeling
267(3)
19.6.2 Principles of energy-efficient MU-MIMO power allocation
270(1)
19.6.3 Energy-efficient MU-MIMO with improved circuit management
271(6)
19.6.4 Energy efficiency gain
277(5)
20 Centralized energy-efficient wireless resource management
282(19)
20.1 Overview
282(3)
20.1.1 Circuit component management
282(1)
20.1.2 Time-domain resource management
283(1)
20.1.3 Frequency-domain resource management
284(1)
20.1.4 Spatial-domain resource management
284(1)
20.2 Energy-efficient OFDMA in flat-fading channels
285(6)
20.2.1 Resource allocation without fairness
287(1)
20.2.2 Resource allocation with fairness
288(1)
20.2.3 Performance comparisons
289(2)
20.3 Energy-efficient scheduling in frequency-selective channels
291(10)
20.3.1 Time-averaged network energy efficiency
292(2)
20.3.2 Energy-efficient scheduler
294(3)
20.3.3 Network performance
297(4)
21 Distributed energy-efficient wireless resource management
301(20)
21.1 Distributed energy-efficient MAC design
301(7)
21.1.1 General rules of distributed MAC design
302(2)
21.1.2 Impact of traffic load on energy consumption
304(4)
21.2 Energy-efficient communications in special regimes
308(4)
21.2.1 Circuit power dominated regime
309(1)
21.2.2 Transmit power dominated regime
309(1)
21.2.3 Noise dominated regime
310(1)
21.2.4 Interference dominated regime
310(2)
21.3 Distributed energy-efficient power control in frequency-selective channels
312(9)
21.3.1 Non-cooperative energy-efficient power optimization game
313(1)
21.3.2 Existence of equilibrium
314(1)
21.3.3 Uniqueness of equilibrium in flat-fading channels
315(1)
21.3.4 Uniqueness of equilibrium in frequency-selective channels
316(1)
21.3.5 Conservative nature of power control
317(1)
21.3.6 Spectral efficiency and energy efficiency improvement
318(3)
22 Energy-efficient cellular network design
321(14)
22.1 Fundamental tradeoffs in network resource utilization
321(6)
22.1.1 Spectral and energy efficiency in single-user systems
322(1)
22.1.2 Spectral and energy efficiency in multi-user systems with orthogonal selective channels
323(2)
22.1.3 Spectral and energy efficiency in multi-user systems with interference channels
325(2)
22.2 Energy-efficient homogeneous network deployment
327(3)
22.3 Energy-efficient heterogeneous network deployment
330(2)
22.4 Energy-efficient cellular network operation
332(3)
22.4.1 Energy-efficient cell breathing
332(1)
22.4.2 Energy-efficient BS sleeping
332(1)
22.4.3 Cell size adaptation techniques
333(1)
22.4.4 Other energy-efficient designs
334(1)
23 Implementation in practice
335(3)
Appendix A Proofs of Theorems and Lemmas 338(17)
References 355(10)
Index 365
Guowang Miao is an Assistant Professor in the Department of Communications Systems at KTH Royal Institute of Technology, Sweden. After receiving his PhD in electrical and computer engineering from Georgia Institute of Technology, USA, in 2009, he spent two years working in industry as a Senior Standard Engineer at Samsung Telecom America. His current research interests are in the design and optimization of wireless communications and networking. Guocong Song is currently Principal Research Engineer at ShareThis, Palo Alto, California. He has been working in wireless communications and networks for a decade, since receiving his PhD in electrical and computer engineering from Georgia Institute of Technology. He received the 2010 IEEE Stephen O. Rice Prize for the best paper in the field of communications theory, and he is recently active in the area of data science and machine learning.
Biežāk uzdotie jautājumi par e-grāmatām Biežāk uzdotie jautājumi par e-grāmatām
Permanent link: https://www.kriso.lv/db/97813161909992e.html
Keywords: