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- Herausgeber: John Wiley & Sons
- Kategorie: Wissenschaft und neue Technologien
- Sprache: Englisch
The third edition of this popular reference covers enabling technologies for building up 5G wireless networks. Due to extensive research and complexity of the incoming solutions for the next generation of wireless networks it is anticipated that the industry will select a subset of these results and leave some advanced technologies to be implemented later,. This new edition presents a carefully chosen combination of the candidate network architectures and the required tools for their analysis. Due to the complexity of the technology, the discussion on 5G will be extensive and it will be difficult to reach consensus on the new global standard. The discussion will have to include the vendors, operators, regulators as well as the research and academic community in the field. Having a comprehensive book will help many participants to join actively the discussion and make meaningful contribution to shaping the new standard.
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Veröffentlichungsjahr: 2016
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Table of Contents
Cover
Title Page
Preface
1 Introduction
1.1 Network Model
1.2 Network Connectivity
1.3 Wireless Network Design with Small World Properties
1.4 Frequency Channels Backup
1.5 Generalized Network Model
1.6 Routing Protocols Over
s
-Lattice Network
1.7 Network Performance
1.8 Node, Route, Topology, and Network Robustness
1.9 Power Consumption
1.10 Protocol Complexity
1.11 Performance Evaluation
1.12 Book Layout
Appendix A.1
References
2 Adaptive Network Layer
2.1 Graphs and Routing Protocols
2.2 Graph Theory
2.3 Routing with Topology Aggregation
References
3 Mobility Management
3.1 Cellular Networks
3.2 Cellular Systems with Prioritized Handoff
3.3 Cell Residing Time Distribution
3.4 Mobility Prediction in Pico- and Micro-Cellular Networks
Appendix A.3 Distance Calculation in an Intermediate Cell
References
4 Ad Hoc Networks
4.1 Routing Protocols
4.2 Hybrid Routing Protocol
4.3 Scalable Routing Strategies
4.4 Multipath Routing
4.5 Clustering Protocols
4.6 Cashing Schemes for Routing
4.7 Distributed QoS Routing
References
5 Sensor Networks
5.1 Introduction
5.2 Sensor Network Parameters
5.3 Sensor Network Architecture
5.4 Mobile Sensor Network Deployment
5.5 Directed Diffusion
5.6 Aggregation in Wireless Sensor Networks
5.7 Boundary Estimation
5.8 Optimal Transmission Radius in Sensor Networks
5.9 Data Funneling
5.10 Equivalent Transport Control Protocol in Sensor Networks
References
6 Security
6.1 Authentication
6.2 Security Architecture
6.3 Key Management
6.4 Security in Ad Hoc Networks
6.5 Security in Sensor Networks
References
7 Network Economics
7.1 Fundamentals of Network Economics
7.2 Wireless Network Microeconomics: Data Sponsoring
7.3 Spectrum Pricing for Market Equilibrium
7.4 Sequential Spectrum Sharing
7.5 Data Plan Trading
References
8 Multi-Hop Cellular Networks
8.1 Modeling Multi-Hop Multi-Operator Multi-Technology Wireless Networks
8.2 Technology Background
8.3 System Model and Notation
8.4 m
3
Route Discovery Protocols
8.5 Performance of m
3
Route Discovery Protocols
8.6 Protocol Complexity
8.7 Traffic Offloading Incentives
8.8 Performance Illustrations
References
9 Cognitive Networks
9.1 Technology Background
9.2 Spectrum Auctions for Multi-hop Cognitive Networks
9.3 Compound Auctioning in Multi-hop Cognitive Cellular Networks
References
10 Stochastic Geometry
10.1 Background Theory
References
11 Heterogeneous Networks
11.1 Preliminaries
11.2 Self-Organized Small Cell Networks
11.3 Dynamic Network Architecture
11.4 Economics of Heterogeneous Networks
References
12 Access Point Selection
12.1 Background Technology
12.2 Network Selection Game
12.3 Joint Access Point Selection and Power Allocation
12.4 Joint AP Selection and Beamforming Optimization
References
13 Self-Organizing Networks
13.1 Self-Organizing Network Optimization
13.2 System Model
13.3 Joint Optimization of Tilts and AP Association
References
14 Complex Networks
14.1 Evolution Towards Large-Scale Networks
14.2 Network Characteristics
14.3 Random Graphs
References
15 Massive MIMO
15.1 Linearly Precoded Multicellular Downlink System
15.2 System Model
15.3 Optimization for Perfect Channel State Information
15.4 Robust Designs for WSRM Problem
Appendix A.15
Appendix B.15
References
16 Network Optimization Theory
16.1 Introduction
16.2 Layering as Optimization Decomposition
16.3 Cross-Layer Optimization
16.4 Optimization Problem Decomposition Methods
References
17 Network Information Theory
17.1 Capacity of Ad Hoc Networks
17.2 Information Theory and Network Architectures
17.3 Cooperative Transmission in Wireless Multihop
Ad Hoc
Networks
References
18 Stability of Advanced Network Architectures
18.1 Stability of Cooperative Cognitive Wireless Networks
18.2 System Model
18.3 System Optimization
18.4 Optimal Control Policy
18.5 Achievable Rates
18.6 Stabilizing Transmission Policies
References
19 Multi-Operator Spectrum Sharing
19.1 Business Models for Spectrum Sharing
19.2 Spectrum Sharing in Multi-hop Networks
References
20 Large Scale Networks and Mean Field Theory
20.1 MFT for Large Heterogeneous Cellular Networks
20.2 Large Scale Network Model Compression
20.3 Mean Field Theory Model of Large Scale DTN Networks
20.4 Mean Field Modeling of Adaptive Infection Recovery in Multicast DTN Networks
20.5 Mean Field Theory for Scale-Free Random Networks
20.6 Spectrum Sharing and MFT
20.7 Modeling Dynamics of Complex System
Appendix A.20 Iterative Algorithm to Solve Systems of Nonlinear ODEs (DiNSE-Algorithm)
Appendix B.20 Infection Rate of Destinations for DNCM
Appendix C.20 Infection Rate for Basic Epidemic Routing
References
21 mmWave Networks
21.1 mmWave Technology in Subcellular Architecture
21.2 Microeconomics of Dynamic mmWave Networks
References
22 Cloud Computing in Wireless Networks
22.1 Technology Background
22.2 System Model
22.3 System Optimization
22.4 Dynamic Control Algorithm
22.5 Achievable Rates
22.6 Stabilizing Control Policies
References
23 Wireless Networks and Matching Theory
23.1 Background Technology: Matching Markets
23.2 Distributed Stable Matching in Multiple Operator Cellular Network with Traffic Offloading
23.3 College Admissions Game Model for Cellular Networks with Traffic Offloading
23.4 Many to Many Matching Games for Caching in Wireless Networks
23.5 Many to One Matching with Externalities in Cellular Networks with Traffic Offloading
23.6 Security in Matching of Device to Device Pairs in Cellular Networks
References
24 Dynamic Wireless Network Infrastructure
24.1 Infrastructure Sharing in Multi-Operator Cellular Networks
24.2 User Provided Connectivity
24.3 Network Virtualization
24.4 Software Defined Networks
24.5 SDN Security
References
Index
End User License Agreement
List of Tables
Chapter 03
Table 3.3.1 Goodness of fit (
G
) of the approximation given by (3.2.8) for the cumulative distribution function of the channel holding time in a cell
Chapter 04
Table 4.2.1 Simulation parameters
Table 4.3.1 Node density
Table 4.7.1 The data structure carried by a probe
p
Chapter 05
Table 5.2.1 Frequency bands available for ISM applications
Table 5.3.1 Categorization of MAC protocols
Chapter 06
Table 6.2.1 Special
Z
symbols
Table 6.3.1 Generic point to point key distribution
Table 6.3.2 Point to point key distribution
Table 6.3.3 Point to point key distribution
Chapter 08
Table 8.8.1 Description of the scenarios
Table 8.8.2 Offloading scenarios as shown in Figure 8.8.5
Chapter 11
Table 11.3.1 Simulation parameters
Table 11.3.2 GA parameters
Table 11.3.3 Topology reconfiguration scenarios
Chapter 17
Table 17.3.1 Simulation parameters
Chapter 21
Table 21.1.1 Probability mass function of
D
ℓ
and
[1]
List of Illustrations
Chapter 01
Figure 1.1.1 Macro cell tessellation
Figure 1.2.1 Connectivity alternatives (the direction of the adjacent users is chosen in increasing order of distance from the BS)
Figure 1.3.1
s-Lattice
parameters
Figure 1.3.2
s-Lattice
connection model for: (a) user 2 and (b) user 3
Figure 1.3.3
2ws-Lattice
Figure 1.5.1 Connectivity alternatives for the generalized model (the direction of the adjacent users is chosen randomly)
Figure 1.10.1 Transitions of the route discovery protocol from a given subcell
i
to its neighboring cells
jk
Figure 1.11.1
l
given by (1.7.8) versus N for
p
= 0.5 and different 2 layer (2L) protocols
Figure 1.11.2 Average
τ
versus
N
for different
p
wlan
Figure 1.11.3 Clustering coefficient versus
p
and
N
= 200
Figure 1.11.4 Average node robustness versus
p
for one layer and two layer protocols (
H
= 4)
Figure 1.11.5 Route robustness
B
versus the subcell index for
p
= 0.5
Figure 1.11.6 Network robustness
ξ
l
versus the number of frequency channels
k
f
where
ξ
is given by (1.3.1).
c
= 50,
n
p
= 20,
b
p
= 10,
n
s
= 25,
b
s
variable,
p
= 0.5 and 0.9,
l
= 5.72,
p
= 0.5, 2LR protocol
Figure 1.11.7 Power consumption versus
N
Figure 1.11.8 Complexity Δ versus the subcell index for
p
= 0.5
Figure 1.11.9 Utility versus
s
for different values of
p
return
and
k
f
= 7
Figure 1.11.10 Utility defined by (1.4.5a) versus
s
for
p
return
= 0.7 and
n
s
= 10
Chapter 02
Figure 2.1.1 Directed graph
Figure 2.1.2 Undirected graph
Figure 2.1.3 Weighted graphs
Figure 2.1.4 Illustration of a walk
Figure 2.1.5 Complete graphs: (a) V nodes and V(V – 1) edges: three nodes and 3 × 2 edges. (b) V nodes and V(V – 1)/2 edges: four nodes and 4 × 3/2 edges
Figure 2.1.6 Connected graphs
Figure 2.1.7 Bipartite graph
Figure 2.1.8 Tree
Figure 2.1.9 Spanning trees
Figure 2.1.10 Minimum spanning tree
Figure 2.1.11 MST solution via Prim’s algorithm
Figure 2.1.12 MST solution via Kruskal’s algorithm
Figure 2.1.13 Example of the distributed algorithm
Figure 2.1.14 Examples of minimum spanning tree and shortest path spanning tree
Figure 2.1.15 Example of Dijkstra algorithm
Figure 2.1.16 A MST for the basic graph in Figure 2.1.15
Figure 2.1.17 (a) An illustration for Bellman–Ford algorithm (b) An example of Floyd–Warshall algorithm
Figure 2.1.18 Distributed Bellman–Ford algorithm example
Figure 2.1.19 Simple rerouting case
Figure 2.1.20 Routing loop case
Figure 2.1.21 Count to infinity problem
Figure 2.1.22 Split Horizon algorithm
Figure 2.1.23 Example where Split Horizon fails
Figure 2.3.1 Network example
Figure 2.3.2 Aggregated network from Figure 2.3.1 with a complete view of domain A
Figure 2.3.3 Topology aggregation. (a) Domain F. (b) Mesh of the borders. (c) Star representation. (d) Star representation with bypasses
Chapter 03
Figure 3.1.1 Components of location management process
Figure 3.1.2 Components of handoff management
Figure 3.1.3 Location management SS7 signaling network
Figure 3.1.4 Location registration procedures
Figure 3.1.5 Call delivery procedures
Figure 3.1.6 Pointer forwarding strategy
Figure 3.1.7 Local anchoring scheme
Figure 3.1.8 Time-based location update scheme
Figure 3.1.9 Movement-based location update scheme
Figure 3.1.10 Distance-based location update scheme
Figure 3.1.11 Mobile IP architecture
Figure 3.1.12 Mobile IP location management
Figure 3.1.13 Mobile IP location registration
Figure 3.1.14 Mobile IP location management operations
Figure 3.1.15 Mobile IP smooth handoff with fresh binding at previous FA
Figure 3.1.16 Mobile IP smooth handoff without fresh binding at previous FA
Figure 3.1.17 ATM location management techniques
Figure 3.1.18 Two-tier database scheme
Figure 3.1.19 LR Hierarchy: WATM LRs scheme
Figure 3.1.20 WATM handoff management techniques
Figure 3.2.1 State-transition diagram for Channel Reservation – CR handoffs
Figure 3.2.2 Call flow diagram for channel reservation with queueing – CRQ handoffs
Figure 3.2.3 State-transition diagram for CRQ Priority Scheme
Figure 3.2.4 Blocking and forced termination probabilities for CRQ Priority Scheme
Figure 3.2.5 Blocking and forced termination probabilities for CRQ systems with 20 channels/cell,
R
= 2 mi
Figure 3.2.6 Blocking and forced terminations for priority CR and CRQ schemes (20 channels/cell, one handoff channel/cell,
R
= 2 mi)
Figure 3.3.1 Illustration of distance from point A in cell (where call is originated), to point C on cell boundary (where mobile exits from cell)
Figure 3.3.2 Illustration of distance from cell entering point (A on cell boundary), to to cell exiting point (C on cell boundary)
Figure 3.3.3 Mean channel holding time (s) in cell versus R (average call duration = 120 s)
Figure 3.4.1 Parameters used to calculate the directional probability
Figure 3.4.2 Definition of the MLC
Figure 3.4.3 Distance Y in originating cell
k
Figure 3.4.4 Distance Y in an intermediate cell
k
Figure 3.4.5 Blocking ratio in three systems
Figure 3.4.6 Utilization in three systems
Chapter 04
Figure 4.1.1 Illustration of dynamic topology
Figure 4.1.2 Proactive/timer: route updates routes -----, data
Figure 4.1.3 Reactive/on demand: route REQ , route REP , data flow
Figure 4.1.4 (a) Proactive/timer protocol operation when the topology is changed. (b) Reactive/on demand protocol operation when the topology is changed
Figure 4.1.5 Classification of ad hoc routing protocols. ABR, associativity based routing; AODV, ad hoc on-demand distance vector; CGSR, cluster head gateway switch routing; DSDV, destination-sequenced distance-vector; DSR, dynamic source routing; LMR, lightweight mobile routing; SSR, signal stability-routing; TORA, temporally ordered routing algorithm; WRP, wireless routing protocol
Figure 4.1.6 Illustration of route updating process
Figure 4.1.7 Illustration of route updating process after A moves
Figure 4.1.8 DSR – route discovery
Figure 4.1.9 DSR – route discovery decision process at source A
Figure 4.1.10 DSR – route discovery decision process at an intermediate node
Figure 4.1.11 DSR – optimizations
Figure 4.1.12 Learning about the routs by “listening.”
Figure 4.1.13 AODV – path finding
Figure 4.1.14 Temporally order routing algorithm (TORA)
Figure 4.1.15 TORA – route creation
Figure 4.1.16 TORA – route creation (visualization)
Figure 4.1.17 TORA – maintaining routes link failure with no reaction
Figure 4.1.18 TORA – re-establishing routes after link failure of last downstream link
Figure 4.2.1 A routing zone of radius
r
= 2 hops
Figure 4.2.2 Illustration of IERP operation
Figure 4.2.3 Guiding the search in desirable directions
Figure 4.2.4 Loop-back termination
Figure 4.2.5 Early termination
Figure 4.2.6 Query detection (QD1/QD2)
Figure 4.2.7 Selective broadcasting
Figure 4.2.8 The position of the routing functions in the protocol stack
Figure 4.2.9 IARP traffic generated per neighbor
Figure 4.2.10 ZRP traffic per node (
N
= 1000 nodes,
v
= 0 : 5 neighbors/s)
Figure 4.3.1 The network hierarchical structure
Figure 4.3.2 Three layers network hierarchical structure
Figure 4.3.3 Physical multilevel clustering
Figure 4.3.4 System performance versus the routing information refresh rate with
v
= 90 km/h
Figure 4.3.5 Optimum routing information refresh rate versus mobility
Figure 4.3.6 Illustration of a fisheye
Figure 4.3.7 Message reduction using fisheye
Figure 4.3.8 Control O/H versus number of nodes
Figure 4.5.1 Distributed clustering algorithm (cluster ID – cid) [65]
Figure 4.5.2 System topology
Figure 4.5.3 Clustering
Figure 4.5.4 Ad hoc mobility model node movement: (a) epoch random mobility vectors (b) and hoc mobility model node movement
Figure 4.5.5 Simulation results: (a) cluster size (
R
= 1000 m), (b) cluster size (
R
= 500 m), (c) cluster survival (
R
= 1000 m), (d) cluster survival (
R
= 500 m)
Figure 4.5.6 Logical relationships among MANET network layer entities
Figure 4.6.1 An example of values of data structures used in C-ZRP,
k
= 2
Figure 4.6.2 An example of interzone path creation and deletion. (a) Node
B
2
injects the external routes to nodes
S,B1,B3,B4,D
into its zone. (b) Node
Y
creates a set of entries into its own EZT. (c)
Y
now has two routes to node.
B1
Figure 4.6.3 An example of (a) injection and (b) deletion of external nodes
Figure 4.7.1 Generation of probes
p
(number of tokens)
Figure 4.7.2 Two token curves. Curve (a) is chosen for simplicity and efficient computation. Curve (b) determines the number of optimization tokens
O
, based on the delay requirement
D
Figure 4.7.3 Two token curves as functions of
B
Figure 4.7.4 Success ratio. Imprecision rate: (a) 5% and (b) 50%
Figure 4.7.5 Messages overhead (imprecision rate: 10%)
Figure 4.7.6 Cost per established path (imprecision rate: 5%)
Figure 4.7.7 Cost per established path (imprecision rate: 50%)
Chapter 05
Figure 5.1.1 Battlefield surveillance
Figure 5.2.1 Sensor network topology
Figure 5.3.1 The sensor networks protocol stack
Figure 5.3.2 Classification of MAC schemes
Figure 5.3.3 Multi-hop routing due to limited transmission range
Figure 5.3.4 Broadcasting an interest (are there any soldiers in the area?) and advertising (there are soldiers in the area)
Figure 5.3.5 Data aggregation
Figure 5.3.6 Data funneling: (a) Setup phase (b) Data communication phase
Figure 5.3.7 Event to sink reliable transport (ESRT)
Figure 5.5.1 Diffusion: (a) gradient establishment, (b) reinforcement, (c) multiple sources, (d) multiple sinks, (e) repair
Figure 5.6.1 An admissible flow network
G
with lifetime 50 rounds and two aggregation trees
A
1
and
A
2
with lifetimes 30 and 20 rounds respectively. The depth of the schedule with aggregation trees
A
1
and
A
2
is 2
Figure 5.7.1 Sensing an inhomogeneous field. (a) Points are sensor locations. The environment has two conditions indicated by the gray and white regions of the square. (b) The sensor network domain is partitioned into square cells. (c) Sensors within the network operate collaboratively to determine a pruned partition that matches the boundary. (d) Final approximation to the boundary between the two regions which is transmitted to a remote point
Figure 5.7.2 Effect of sensor network density (resolution) on boundary estimation. Column 1 – noisy set of measurements, column 2 – estimated boundary, and column 3 – associated partition [118]
Figure 5.8.1 Different regions to calculate the number of neighbors versus the transmission radius of the node
Figure 5.8.2 The overlapping area between the transmission radius coverage and the effective area of the network
Figure 5.8.3 Analytical curve for the settling time, which is the sum of the reception and contention times
Chapter 06
Figure 6.1.1 Authentication protocols: (a) one-way authentication of A, (b) one-way authentication of B, (c) two-way authentication, (d) two-way authentication with three messages
Figure 6.1.2 An oracle session attack
Figure 6.1.3 Improved two-way protocol
Figure 6.1.4 A parallel session attack
Figure 6.1.5 An asymmetric two-way authentication
Figure 6.1.6 An offset attack (through a parallel session)
Figure 6.1.7 P1: Resistance to replay attacks through use of nonces. P2: Use of symmetric cryptography. P3: Resistance to parallel session attacks. P4: Resistance to selected text and offset attacks. P5: Minimal number of encryptions
Figure 6.1.8 (a) Operation of public key encryption system. (b) Numerical example of operation of public key encryption system
Figure 6.4.1 The configuration of a key management service
Figure 6.4.2 Threshold signature with three servers
Figure 6.4.3 The creation of nodes’ updated and non-updated repositories (where
). See the text box for an explanation of steps 1–3
Figure 6.4.4 A certificate graph and paths of certificates between users
u
and
v
in their merged updated local repositories
Figure 6.5.1 A laptop-class adversary using a wormhole to create a sinkhole in TinyOS beaconing
Chapter 07
Figure 7.1.1 Selfish users choose
x
′, the socially optimal choice is
x
*
Figure 7.1.2 The best response functions intersect at the unique pure-strategy Nash equilibrium (0,
B
/
b
)
Figure 7.1.3 The social welfare under the Nash equilibrium and its maximum value
Figure 7.1.4 Model to study the impact of network neutrality
Figure 7.1.5 Ratios of investments, revenues, and user demand (neutral/non-neutral) as a function of
a
for
v
= 0.5 and
w
= 0.3
Figure 7.3.1 Total profit and solutions of market equilibrium, competitive, and cooperative pricing models.
Chapter 08
Figure 8.3.1 Multi-hop, multi-operator, and multi-technology (m
3
) wireless network
Figure 8.4.1 Relaying alternatives for MDR
Figure 8.4.2 Routing/scheduling for m
3
network by using clustering factor
K
= 7
Figure 8.4.3 Modeling limited interference routing/scheduling (LIR)
Figure 8.4.4 Relaying transmission probabilities for initial state
ss
(1)
Figure 8.4.5 Relaying transmission probabilities for initial state
ss
(2)
Figure 8.5.1 m
3
scenario
Figure 8.6.1 Transitions of the route discovery protocol from a given subcell
i
to its neighboring cells
j
k
for MDR
Figure 8.6.2 Transitions of the route discovery protocol from a given subcell
i
to its neighboring cells
j
k
for LIR
Figure 8.8.1 Δ
i
versus the subcell index
i
for MDR and the scenario shown in Figure 8.4.4
Figure 8.8.2 Δ
i
versus the subcell index
i
for LIR and the scenario shown in Figure 8.4.4
Figure 8.8.3
τ
i
versus the subcell index
i
for MDR and the scenario shown in Figure 8.4.4
Figure 8.8.4
τ
i
versus the subcell index
i
for LIR and the scenario shown in Figure 8.4.4
Figure 8.8.5
B
i
versus the subcell index
i
for the scenario shown in Figure 8.4.4
Figure 8.8.6 Routing/scheduling scenario for m
3
network by using clustering factor
K
= 7
Figure 8.8.7 Network capacity
Figure 8.8.8 Throughput
Figure 8.8.9 m
3
network topology
Figure 8.8.10 Δ
U
and Δ
U
1 versus the price
χ
for scenarios 1 and 3 presented in Figure 8.8.5 and Table 8.8.2
Figure 8.8.11 The optimum price
χ
* for scenarios 1–7 presented in Figure 8.8.5 and Table 8.8.2
Figure 8.8.12 Optimum price
χ
* versus the number of new calls in the WLAN
Chapter 09
Figure 9.2.1 A secondary spectrum market with three SNs and two channels
Figure 9.2.2 Procedure of channel assignment. Dots and squares represent source and destination nodes respectively. (a) Assign channels to SN
a
. (b) Assign channels to SN
b
. (c) Assign channels to SN
d
Figure 9.2.3 Auction efficiency with different number of bidders enrolled
Figure 9.3.1 Network model with N cell reuse pattern for
K
= 7
Figure 9.3.2 (a) Markov model of the different transmission trials in a subcell. (b) Markov model state transition probabilities
Figure 9.3.3 Static group-bidding scheme based on
K
reuse pattern
Figure 9.3.4 Markov model state transition probabilities with m2m connections
Figure 9.3.5 (a)
p
b
′
defined by (9.3.6) versus
b
′, (b)
p
return
defined by (9.3.8) versus
b
′
Figure 9.3.6
p
bs
versus
p
for different values of
b
′
Figure 9.3.7 Delay
τ
versus
p
for different values of
b
′
Figure 9.3.8 Valuation
V
defined by (9.3.13) versus
b
′ for different values of
p
Figure 9.3.9 Utility defined by (9.3.18) versus
b
′ for different values of
p
Figure 9.3.10 Bid defined by (9.3.15) and tip defined by (9.3.16) versus
b
′
Figure 9.3.11 Auction efficiency comparison for different network size
Figure 9.3.12 Overall price versus
b
′
Figure 9.3.13 Utility versus
b
′ when
Figure 9.3.14 Utility obtained for different number of winner groups
S
Figure 9.3.15
β
m
versus subcell index
m
Figure 9.3.16
U
m
versus subcell index
m
Figure 9.3.17 Percentage of truthful and non-truthful bidders
Figure 9.3.18
β
m
versus subcell index
m
Figure 9.3.19
U
m
versus subcell index
m
Figure 9.3.20
p
bs
/
m2m
versus
p
for different values of
b
′
Figure 9.3.21 Delay
τ
versus
p
m2m
Figure 9.3.22 Utility defined by (9.3.18) versus
b
′ when
β
= 0.4,
θ
= 0.6,
γ
= 1/10
Figure 9.3.23 Cost defined by (9.3.14a) versus
b
′ when
β
= 0.4,
θ
= 0.6
Chapter 10
Figure 10.1.1 (a) Cellular network modeled via the PPP, (b) Cellular network modeled via the HCPP
Figure 10.1.2 The network modeled as a weighted Voronoi tessellation (the square shapes represent the macro BSs and the dots represent the small BSs)
Chapter 11
Figure 11.1.1 The network modeled as a weighted Voronoi tessellation (the larger dots represent the MBSs with the coverage within the Voronoi cell, white circles coverage of the small cell)
Figure 11.1.2 The network modeled as a superposition of two independent Voronoi tessellations: the diamond-shaped dots with the dashed Voronoi cells represent the macro network tier, and the circular dots with the solid Voronoi cells represent the small cell network tier
Figure 11.3.1 Dynamic network architecture: (a) possible realization of DNA macro network, (b) clustering DNA macro network, (c) formal cluster separation in DNA network for different frequencies (
f
k
,
k
= 1,2,3), and example of transmission between a cluster of DNA networks
Figure 11.3.2 DNA network model with
T/W
(
r
)/
I
(
Q
) contract with: (a) wired Internet and (b) wireless Internet
Figure 11.3.3 Illustration of the genetic operators: (a) crossover and (b) mutation
Figure 11.3.4 Interaction of the 2L-AAC scheme and the optimization problem
Figure 11.3.5 Simulation scenario with index of user
i
= 1, …,
N
and AP
j
= 1, …,
K
Figure 11.3.6 Utility defined by (11.3.6) for the optimum topology versus
N
Figure 11.3.7 Number of topologies
G
generated to solve (11.3.7) versus
N
Figure 11.3.8 Running time
T
c
needed to obtain the optimum topology by (11.3.7) versus
N
Figure 11.3.9 Utility defined by (11.3.7) for the optimum topology versus
K
and
N
= 6
Figure 11.3.10 Optimum
M
versus
K
for the optimization problem defined by (11.3.7)
Figure 11.3.11
M
and
K
versus the QoS constraint
γ
Figure 11.3.12 Success rate versus
G
when the initial population is randomly chosen
Figure 11.3.13
ξ
i
versus
p
i
I
for static and dynamic model of traffic volume
Figure 11.3.14 Contract price recovery
∊
i
versus
p
i
I
Figure 11.3.15 Dynamic topology and architecture reconfiguration scenarios
Chapter 12
Figure 12.2.1 A network with two access points
Figure 12.2.2 Average throughput (BE) per user in a network with two AP,
n
= 20 strongly asymmetric settings, versus the UDP load
Figure 12.3.1 Comparison of convergence speed by the Si-/Se-JASPA, J-JASPA, and Si-JASPA with connection costs
Figure 12.4.1 Network model with possible connections
Figure 12.4.2 (a) Network partitioned into different clusters. (b) DNA network considered
Figure 12.4.3 Convergence property of the SJOA
Figure 12.4.4 The variation of the achieved sum rate and convergence of the JSBOA over different dynamic behavior of the system
Figure 12.4.5 Sum rate comparison for JSBOA over exhaustive search (Exha) and conventional model (path lost; PL)
Chapter 14
Figure 14.1.1 Network model
Figure 14.1.2 Examples of various types of networks. (a) An undirected network with only a single type of vertex and a single type of edge. (b) A network with a number of discrete vertex and edge types. (c) A network with varying vertex and edge weights. (d) A directed network in which each edge has a direction
Figure 14.3.1 The mean component size (solid line), excluding the giant component if there is one, and the giant component size (dotted line), for the Poisson random graph, Equations 14.3.3 and 14.3.4
Chapter 15
Figure 15.1.1 Antenna patterns for 25 × 25 array
Figure 15.1.2 Antenna patterns for 30 × 30 array
Figure 15.4.1 Illustration of a two-cell system model with four users. The dotted-dashed lines indicate the inter-cell interference, while the solid lines show the broadcast part of the signal transmitted by each BS
Figure 15.4.2 Worst case sum rate of different schemes as a function of
ρ
for box uncertainty set. The zero-forcing strategy is adopted from Refs. [3, 4] at
SNR
= 10 dB
Chapter 16
Figure 16.2.1 Logical topology graph of a network illustrating contention
Figure 16.2.2 Contention graph derived from the logical topology graph shown in Figure 16.2.1
Figure 16.2.3 Bipartite graph between maximal cliques and links in the contention graph from Figure 16.2.2
Figure 16.2.4 Comparison of network utilities in a numerical example
Figure 16.4.1 Schematic illustrating optimization problem decomposition
Figure 16.4.2 Multilevel decomposition
Chapter 17
Figure 17.1.1 A tessellation of the surface
S
2
of the sphere
Figure 17.3.1 Nodes in the ring Δ
r
transmit approximately simultaneously
Figure 17.3.2 Connectivity ratio versus number of nodes in the network
Figure 17.3.3 Delivery ratio versus number of nodes in the network
Figure 17.3.4 End to end delay versus number of nodes in the network
Chapter 18
Figure 18.2.1 Cooperative control options of a cooperative pair (
i, j
) in subframe
n
ij
Figure 18.5.1 Network stability regions
Figure 18.6.1 Histogram of cooperative control
Chapter 19
Figure 19.1.1 Capacity aggregation – a model
Figure 19.1.2 Capacity borrowing/leasing – BL model
Figure 19.1.3 Cognitive networks – C model
Figure 19.1.4 Partial cognitive networks – PC model
Figure 19.1.5 Mutually cognitive networks – MC model
Figure 19.1.6 Spectra aggregation in heterogeneous network: (a) CW mode, (b) CWC mode
Figure 19.1.7 Channel lending with pricing
Figure 19.1.8 Mutual channel leasing with pricing
Figure 19.1.9 Markov model representation of the two-operator voice traffic system
Figure 19.10 (a) BL system model with pricing (b) MBL system model with pricing
Figure 19.1.11 Conditional probability of benefiting for data traffic in A mode for
Figure 19.1.12 Conditional probability of benefiting for voice traffic in A mode for
Figure 19.1.13 Helping probabilities in B/L system for
c
= 20
Figure 19.1.14 Channel corruption probability in cognitive mode for
c
= 20
Figure 19.1.15 The system queue length in aggregate A mode and conventional C mode
Figure 19.1.16 The system waiting time CDF for
c
= 10 in A mode
Figure 19.1.17 Steady state probability density function for the BL system with pricing (voice traffic),
,
and
c
= 10
Figure 19.1.18 2D Steady state probability density function for the BL system with pricing (voice traffic)
Figure 19.1.19 Spectrum utilization factor: (a) analytical results, (b) analytical and simulation results
Figure 19.1.20 Spectra utilization factor in cognitive networks
Figure 19.2.1 Channel lending with pricing
Figure 19.2.2 Mutual channel lending with pricing
Figure 19.2.3 State transition diagram: (a) with DTA, (b) with dropping queue management algorithms, (c) for double threshold queue management algorithm
Figure 19.2.4 Two-dimensional Markov chain state transition diagram for: (a) MCBL model without buffer, (b) ACBL model without buffer
Figure 19.2.5 Averaged-Time model state transition diagram
Figure 19.2.6 Possibilities for transition when buffer is full
Figure 19.2.7 Possibilities for transition when buffer is full
Figure 19.2.8 Joint state probability distribution function for Averaged-Time model with priority operator,
Figure 19.2.9 Joint state probability distribution function for Averaged-Time model with priority operator,
Figure 19.2.10 Joint state probability distribution function for Averaged-Time with priority operator,
Figure 19.2.11 Average packet dropping rate for Averaged-Time model with priority operator,
Figure 19.2.12 Joint state probability distribution function for Averaged-Time model with proportional drop
,
Figure 19.2.13 Joint state probability distribution function for Averaged-Time model with proportional drop
,
Figure 19.2.14 Joint state probability distribution function for Averaged-Time model with proportional drop
,
Figure 19.2.15 Joint state probability distribution function for Averaged-Time model with proportional drop
,
Figure 19.2.16 Average packet dropping rate for Averaged-Time model with proportional dropping algorithm,
Chapter 20
Figure 20.2.1 The local model describing the set of states
ℵ
Figure 20.4.1 Polymorphic infection process
Figure 20.4.2 Heterogeneous DTN architecture
Figure 20.4.3 Average delivery delay versus
D
Figure 20.4.4 Average delivery delay
and lifetime
L
versus
D
Figure 20.4.5 Average number of copies
E
[
G
L
] and
versus
D
Figure 20.4.6 Infection rate of destinations infected by
f
,
D
f
(
t
), versus
t
for DNCM
Figure 20.4.7 Average delivery delay
R
a
(
t
) versus
t
for: (a) immune, (b) immune TX, (c) vaccine for different values of
p
r
(
t
), and (d) timeout recovery scheme
Figure 20.4.8 Average delivery delay
R
f
(
t
) versus
t
for (a) immune, (b) immune TX, (c) vaccine for different values of
p
r
(
t
), and (d) timeout recovery scheme
Figure 20.4.9 Average delivery delay
E
[
T
f
D
] versus
t
for (a) immune, (b) immune TX, (c) vaccine for different values of
p
r
(
t
), and (d) timeout recovery scheme
Figure 20.4.10 Average delay
versus
D
for different percentages of mobile users
m
Figure 20.4.11 Average delay
versus
D
for immune_TX and different values of
p
r
(
t
)
Figure 20.5.1 Illustrations of network growth: (a) Erdos–Renyi (ER) model, (b) Watts–Strogatz (WS) model
Figure 20.5.2 BA model for
m
0
= 3 and
m
= 2
Figure 20.5.3 BA/A model for
m
0
= 3 and
m
= 2
Figure 20.5.4 BA/B model for
m
0
= 3 and
m
= 2
Figure 20.7.1 Connectivity of nodes of time varying relevancy
r
(
t
) over time for
α
= 1 and 4
Figure 20.7.2 Connectivity of nodes of time varying relevancy
r
(
t
) in time with
Figure 20.7.3 Preferred attachments by relevancy (
r
+
attachments) and previous connectivity (
r
–
attachments)
Figure 20.7.4 Time variation of the connectivity
Figure 20.7.5 Time variation of the network connectivity mismatch
Figure 20.7.6 Average network connectivity mismatch
Chapter 21
Figure 21.1.1 Network model, the beamforming patterns
G
M
,
m
,
θ
(
ϕ
), where
M
is the main lobe directivity gain,
m
is the back lobe gain,
θ
is the beamwidth of the main lobe, and
ϕ
is the angle off the boresight direction
Figure 21.2.1 Interference model
Figure 21.2.2 Spatial selection probability of
AP
i
by terminals versus the average sharing rates of competing APs
Figure 21.2.3 The spatial revenue of operator versus incentive rate
Figure 21.2.4 The spatial revenue of operator versus user density (
λ
u
)
Chapter 22
Figure 22.2.1 Cloud with a VDC
Chapter 24
Figure 24.3.1 A Web services based SOA implementation
Figure 24.3.2 Illustration of a network virtualization environment
Figure 24.3.3 Service-oriented network virtualization
Figure 24.4.1 SDN Network Architecture
Guide
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ADVANCED WIRELESS NETWORKS
TECHNOLOGY AND BUSINESS MODELS
Third Edition
Savo Glisic
University of Oulu, Finland
This edition first published 2016© 2016 John Wiley & Sons, Ltd.
First Edition published in 2006
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Preface
Wireless communications has been developed so far through generations 1G to 4G with exclusive focus on improving the physical layer. This concept has at least two drawbacks: first, wireless channels cannot compete with optical networks when it comes to network capacity; second, the advantages of user mobility have not been emphasized enough. In the scenarios of future dense networks with a significant increase of user terminals and access points, wireless links in the wireless access concept in 5G will become shorter and shorter, asking for more frequent handoffs which jeopardize the reliability of the connections.
A significant part of the future networks will handle Internet of Things and People (IoTP) communications, where sophisticated physical layer solutions cannot be used. Human body implants will use simple solutions. For these reasons there is a common understanding that 5G will be about wireless networks rather than about wireless access to the networks. In the research of the enabling technologies for 5G, different communities focus on different solutions. Small cell technology, mmWave physical layer, cognitive networks, massive MIMO, spectra and infrastructure sharing in multi-operator network management, dynamic network architecture, user provided networks, and so on.
In the design and analysis of these networks a number of powerful analytical tools are used, like: convex, dynamic and stochastic optimization, stochastic geometry, mean field theory, matching theory, and game theory, as well as a number of tools used in economics/microeconomics.
This book advocates a concept where all these technologies will be simultaneously present in the future wireless networks and focuses on three main issues:
Design of heterogeneous networks that include all or a number of these technologies at the same time.
Optimization of such complex networks.
Design of efficient business models to exploit the limited resources of these networks.
Hence the subtitle of this book: Technology and Business Models.
The book is dedicated to the young generation of open-minded researchers, network designers, and managers who will make it happen.
Savo Glisic
1Introduction: Generalized Model of Advanced Wireless Networks
In the process of evolving towards 5G networks, wireless networks are becoming more complex in both, the number of different functionalities they provide as well as in the number of users they serve [1]. Future 5G networks are expected to be highly heterogeneous (see Chapter 11) and to integrate cognitive network concepts [2, 3] (Chapter 9), heterogeneous solutions for the offload of cellular network traffic to WLANs [4, 5], multi-hop cellular networks (Chapter 8) including combinations of ad hoc (Chapter 4) and cellular networks [6, 7], and mobile to mobile (m2m) communications [8]. In order to analyze and control these networks, evolving towards complex networks structures, efficient modeling tools are needed.
Complex network theory (Chapter 14) has emerged in recent years as a powerful tool for modeling large topologies observed in current networks [9]. For instance, the World Wide Web behaves like a power-law node degree distributed network, wireless sensor networks like lattice networks, and relations between social acquaintances like small world networks. The concept of small world networks was first introduced by Watts and Strogatz [10] where a small world network is constructed via rewiring a few links in an existing regular network (such as a ring lattice graph). Later on, Newman-Watt [11] suggested a small world network constructed by adding a few new links (shortcuts) without rewiring existing links. The concept of small world can be introduced to wireless networks, typically to reduce the path length, and thus provide better throughput and end to end delay.
Several works have addressed the question of how to construct a wireless network topology in ad hoc and sensor networks (Chapter 5) in such a way that the small world feature is preserved [12–16]. Long range shortcuts can be created by adding wired links [17], directional beamforming [18] or using multiple frequency channels [19] concepts. In Ref. [9] it was demonstrated that small world networks are more robust to perturbations than other network architectures. Therefore, any network with this property would have the advantage of resiliency where the random omission of some vertices does not increase significantly the average path length or decrease the clustering coefficient. These features are highly desirable in future wireless networks where the availability of links and nodes can be uncertain. For these reasons, in this book we are interested to redesign heterogeneous wireless networks by including small world properties and frequency channels backups.
The considered network model, that we envision for 5G and further to 6G, includes the multi-hop concept to model future networks with dense user populations and enables mobile to mobile (m2m) connections which are already standardized. We see multi-hop cellular networks as an extension or generalization of the existing m2m concept. The potential users acting as relays may belong to different operators and as such may or may not want to cooperate. Consequently, the existence of those links will be uncertain. Some subareas of the cell will be covered by other technologies such as femto cells, small cells, or WLANs enabling the possibility for the cellular system to offload the traffic. The existence of those links depends on the relaying distance and coverage of the WLAN, as well as the cooperation agreement between the operators. In such a complex network, cognitive links might also be available with limited certainty due to unpredictable activity of the primary user (PU). Complex network theory will be used to aggregate all these characteristics of the network into a unified model enabling a tractable analysis of the overall system performance.
Despite of the extensive work in each of the previous fields, to the best of our knowledge, our book is the first to provide a unified model of the network that will include simultaneously all those technologies. The dynamic characteristics of the network results into a dynamic network topology. The work developed by [20] represents the first attempt to model the link uncertainty by complex networks concepts, although in this work, the uncertainty was a consequence only of fading and dynamic channel access. More specifically, our book emphasizes the following aspects of the design and analysis of complex heterogeneous wireless networks:
A unified model for heterogeneous wireless complex networks based on the probabilistic characterization of the node/link uncertainty. The model captures the existence of uncertain and time varying links and nodes inherently present in the latest solutions in wireless networks.
Analytical tools for the unified analysis of the multi-operator collaboration, m2m transmission, different traffic offloading options, and channel availability in cognitive heterogeneous networks.
Redesign of heterogeneous networks by using specific techniques to systematically add, in a controlled way, network redundancy in order to increase the network robustness to link/node failures.
Traffic distribution aware rewiring of the heterogeneous network.
A set of new routing protocols for such network.
Comprehensive analysis of the network in terms of average path length, clustering, robustness, power consumption, and complexity.
In this introduction we start with a general model of the future wireless network, referred to as generic network model, and later in separate chapters we elaborate in more detail each component of such network.
1.1 Network Model
We start by considering a macro cellular network where users transmit uplink by relaying to their adjacent users (neighbors) on the way to the base station (BS). Multi-hop transmission is modeled by considering a virtual cell tessellation scheme presented in Figure 1.1.1, where the macro cell of radius R is divided into inner hexagonal subcells of radius r < R. This partition is not physically implemented in the network but rather used to capture the mutual relations between the terminals in the cell that are potentially available for relaying each other’s messages. For this purpose, it is assumed that, if available, a potential, ready to cooperate transmitter/receiver is on average situated in the center of each subcell.
Figure 1.1.1 Macro cell tessellation
We assume that within a cell the BS is surrounded by H concentric rings of subcells. For the example in Figure 1.1.1, H = 3. The shortest path (in hop count) between the user location and the BS is given by the hop index h, h = 1, … , H. Due to the terminal unavailability, there may be routes towards the BS where the length of the path is longer than h. The number of subcells per ring is nh = 6∙h and the number of subcells per cell is N = 3H(H + 1).
In the sequel, we present a number of characteristics of heterogeneous networks that lead to the uncertain existence of nodes and links. Node percolation will be used to model and quantify the unavailability of users to relay as a consequence of lack of coverage or terminals belonging to a different operator with no mutual agreement for cooperation. When cognitive links are used, link percolation is used to model the link unavailability due to the return of the PU to the channel. These options will be elaborated in detail in the subsequent subsections.
1.1.1 Node Percolation
1.1.1.1 Multiple Operator Cooperation in Cellular Network
Here we model the scenario where a number of operators coexist in the cellular network. It is assumed that a single operator i has a terminal available in a given subcell with probability . In a multi-operator cooperative network, a terminal will be available for relaying in the same subcell if at least one operator has a terminal at that location. This will occur with probability .
This probability is higher for higher number of operators willing to cooperate. In general, this will result into a reduction of the relaying route length. If the operators cooperate and let their users to flexibly connect to the BS that is more convenient to them, the network capacity of both operators will be improved. Thus, a better performance of the network will be obtained in the multi-operator cooperative scenario, as will be shown later in this chapter. The node unavailability for the message forwarding in complex network terminology is referred to as node (or site) percolation.
1.1.1.2 Multiple Operators in Cooperation with Multiple Technologies
In general multiple technologies will be available in a heterogeneous network. Each technology has its own characteristics which enables more appropriate AP choice at a specific place and time based on the users’ requirements. Figure 1.1.1 shows an example of a cellular network overlapping in coverage with a WLAN. In the analysis, we will be interested to generalize this model as follows. The relative coverage between the cellular network and other access technologies, that is WLAN will be characterized by probability pwlan which is the probability that in the next hop the connection will have the opportunity to make a handoff to a different technology and so, terminate the route. The probability pwlan = A/A is calculated as the ratio between the coverage areas of other technologies Ah and the coverage area of cellular network Ac. This can be easily generalized to introduce other traffic offloading options like small/femto cells or other multitier elements like micro and pico cells.
1.1.1.3 Modeling m2m Links
In the analysis, we will consider the possibility that every next relay on the route will be a final destination of an m2m link with probability pm2m. This parameter depends on the probability that the session is within the same cell and parameter N representing the number of subcells in the network.
The simplest model will assume that for a specific session pm2m (Nm2m/N)/Nm2m = 1/N, where Nm2m is the average number of m2m connections per cell. Nm2m/N represents the probability that the given adjacent node is a sink for an m2m connection and 1/N is the probability that it is a sink for a specific session out of Nm2m such sessions.
1.1.2 Link Percolation—Cognitive Links
In the case that cognitive links are used for relaying, which means that we are establishing the routes for the secondary users (SUs; belonging to a secondary operator, SO), there are two related problems that should be considered. The first one is the link availability at the moment when routing/relaying decision is being made and the second one is the PU return probability that will interrupt the ongoing relaying and force the SU user to try it again with a new option.
We assume that spectrum sensing is perfect [3]. Since this problem belongs to the physical layer technology and has been extensively covered in the literature we will not discuss it within this book. We also consider that due to the uncertainty of the PU’s activities, the SO cannot obtain spectrum availability information in advance for the entire message transmission period. We model this uncertainty by defining a probability of return of the PU to the channel currently allocated to the SU, denoted as preturn.
Let us assume that call/data session arrivals follow a Poisson distribution with rate λp and λs for the PU and SU, respectively. The average probability that in a given moment np out of c channels are being used in PO network (the system is in state np) can be obtained as a solution of birth death equations for conventional M/M/c system for data session and M/M/c/c system for voice applications [21].
We assume that the average service time of the SU is 1/μs so that, the probability of having kp new PU arriving within that time is [21]
The probability that a specific channel among c – np
