Wireless Network Design: Optimization Models and Solution ...

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12 Simulation-Based Methods for Network Design 279 (a) OPNET graph representing multiple scalar values (multiple seeds) for each abscissa value (b) Graphical confidence interval representation in the OPNET Modeler Simulation framework. (c) WiMaX Delay 80% Confidence Seeds. Fig. 12.5 Confidence interval representation in OPNET.
280 Marina Aguado, Jasone Astorga, Nerea Toledo and Jon Matias 12.4 Survey of Simulation for Wireless Network Design One common use of simulation techniques is network planning. In this sense, the authors in [26] make use of simulation for CDMA network planning. More precisely, they propose simulation for offline coverage and load evaluation in order to determine a candidate network’s efficiency in the physical layer when the overall service demand exceeds the capacity of the network. Such an evaluation is essential to determine prior to deployment if a potential network configuration can provide adequate physical performance for different numbers of users, located in different points over time. Similarly, in [11] the OPNET simulation tool is used to perform a simulation of the network-traffic characteristics that a mobile WiMAX network to be deployed in a near future will have to face. Additionally, a demand estimation and a VoIP performance assessment is also carried out. The obtained results are crucial for the successful cell and network-capacity planning of any operator interested in deploying a WiMAX mobile network. Regarding network planning, the use of simulation is not restricted to communications networks or applications; it can be used in other research fields. For example, for efficient power system expansion planning, it is essential to evaluate and minimize the total cost of the grid expansion including the construction costs associated with investing in new transmission lines, the operation costs of generators, and the stand-by costs which are the auxiliary service costs for reliable energy supply. Therefore, the authors in [6] propose a new cost simulation methodology which allows for efficient and reliable cost evaluation. Another important application field for which simulation is essential is the evaluation of novel and emerging approaches or technologies which have not been implemented yet in the real world. Following this approach, the authors in [3] use the ns2 simulation tool 2 to prove the effectiveness of a novel protocol designed for efficient power management in wireless sensor networks designed for periodic data acquisition. Although sensor networks have a great potential and can be used for countless applications, they must still surmount a key challenge before their massive deployment becomes a reality: effective energy management. Most sensors operate on non-rechargeable batteries, and thus the lifetime of the sensor network is bounded by the duration of the sensor batteries. The authors confirm the reliability of the results obtained by simulation by an experimental evaluation in a real testbed. Another example is given by [18], where the authors propose a novel architecture for interworking the emerging WiMAX mobile technology with the already deployed 3G cellular networks built around technologies such as UMTS and CDMA2000 using IP Multimedia Subsystem (IMS). As they argue, such an architecture is essential to make the initial deployment of WiMAX networks easier and thus make the real world exploitation of this technology more feasible. In order to validate the performance of the proposed architecture, the authors present an analytical evaluation of crucial parameters such as handover delay, packet loss, jitter, 2 The ns2 tool is available for free online at http://nsnam.isi.edu/nsnam/index.php/User Information.
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International Series in Operations
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Editors Jeff Kennington Eli Olinick
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vi Preface assistance. The work of
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viii Contents 3.4.1 Experiments to
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x Contents 8.3.2 The Solution Proce
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xii Contents References . . . . . .
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List of Contributors Khaldoun Al Ag
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List of Contributors xvii Nitin Sal
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Chapter 1 Introduction to Optimizat
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1 Introduction to Optimization in W
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1 Introduction to Optimization in W
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Part I Background
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10 Dinesh Rajan The goal of this ch
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12 Dinesh Rajan The source encoder
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14 Dinesh Rajan ping (FH) CDMA, and
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16 Dinesh Rajan of SNR is given in
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18 Dinesh Rajan 2.3 Information The
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20 Dinesh Rajan Perror ≤ e −nE
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22 Dinesh Rajan For instance, for t
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24 Dinesh Rajan 2.4.1 Block Codes I
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26 Dinesh Rajan B(z) = 1 � (1 + z
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28 Dinesh Rajan layers. Two of the
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30 Dinesh Rajan nel can support a p
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32 Dinesh Rajan 2.5.2 Physical laye
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34 Dinesh Rajan matched filter for
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36 Dinesh Rajan codes) leads to bet
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38 Dinesh Rajan a length N Inverse
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40 Dinesh Rajan the transmission of
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42 Dinesh Rajan setting, a node may
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44 Dinesh Rajan weighting of the si
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46 Dinesh Rajan 29. Oestges, C., Cl
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48 K. V. S. Hari terrain of operati
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50 K. V. S. Hari fading is calculat
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52 K. V. S. Hari terms have been pr
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54 K. V. S. Hari Fig. 3.1 Normalize
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56 K. V. S. Hari Table 3.3 Impulse
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58 K. V. S. Hari where m antennas a
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60 K. V. S. Hari 3.5.2.4 Dominant R
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62 K. V. S. Hari March, 1984-13 Sep
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64 K. V. S. Hari 47. Van Rees, J.:
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66 J. Cole Smith and Sibel B. Sonuc
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Part II Optimization Problems for N
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102 Eli Olinick carrying capacity.
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104 Eli Olinick 93 85 160 16 38 21
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106 Eli Olinick Amaldi et al. [7] p
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108 Eli Olinick levels (possibly le
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110 Eli Olinick gmℓ ∑ ∑ m∈M
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112 Eli Olinick Using bk to denote
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114 Eli Olinick 5.3.5 Additional De
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116 Eli Olinick capacity, provide n
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118 Eli Olinick ficult optimization
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120 Eli Olinick and-bound so that t
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122 Eli Olinick solution times. Cai
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124 Eli Olinick 27. Glover, F.: Tab
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Chapter 6 Optimization Based WLAN M
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6 Optimization Based WLAN Modeling
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Chapter 7 Spectrum Auctions Karla H
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7 Spectrum Auctions 149 full value.
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7 Spectrum Auctions 151 also decide
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7 Spectrum Auctions 153 disclosed t
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7 Spectrum Auctions 155 focused on
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7 Spectrum Auctions 157 bidders to
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7 Spectrum Auctions 159 in the regi
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7 Spectrum Auctions 161 we suggest
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7 Spectrum Auctions 163 pays, since
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7 Spectrum Auctions 165 prior round
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7 Spectrum Auctions 167 and constra
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7 Spectrum Auctions 169 lem has sup
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7 Spectrum Auctions 171 bidder on a
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7 Spectrum Auctions 173 some discus
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7 Spectrum Auctions 175 21. Dunford
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Chapter 8 The Design of Partially S
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8 The Design of Partially Survivabl
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Part III Optimization Problems in A
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200 Khaldoun Al Agha and Steven Mar
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Chapter 10 Compact Integer Programm
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10 Integer Programming Models for P
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Page 317 and 318: 296 Hang Jin and Li Guo formance in
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Page 321 and 322: 300 Hang Jin and Li Guo ARQ (HARQ)
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Chapter 15 Major Trends in Cellular
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15 Major Trends in Cellular Network
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