Wireless Network Design: Optimization Models and Solution ...

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9 Routing in Mobile Ad Hoc <strong>Network</strong>s 217 17. Perkins, C., Bhagwat, P.: Highly dynamic Destination-Sequenced Distance-Vector routing (DSDV) for mobile computers. ACM SIGCOMM Computer Communication Review 24, 234–244 (1994) 18. Sivakumar, R., Sinha, P., Bharghavan, V.: CEDAR: a Core-Extraction Distributed Ad hoc Routing algorithm. IEEE Journal on Selected Areas in Communications 17, 1454–1465 (1999) 19. Wu, C., Hou, T.: The impact of RTS/CTS on performance of wireless multihop ad hoc networks using IEEE 802.11 protocol. IEEE International Conference on Systems, Man <strong>and</strong> Cybernetics 4, 3558–3562 (2005) 20. Xiao, H., Seah, W., Lo, A., Chua, K.: A flexible quality of service model for mobile ad-hoc networks. IEEE VTC-spring 1, 445–449 (2000)
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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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Page 221 and 222: 200 Khaldoun Al Agha and Steven Mar
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Page 243 and 244: 222 Dag Haugland and Di Yuan that a
Page 245 and 246: 224 Dag Haugland and Di Yuan tions
Page 247 and 248: 226 Dag Haugland and Di Yuan scheme
Page 249 and 250: 228 Dag Haugland and Di Yuan trees:
Page 251 and 252: 230 Dag Haugland and Di Yuan As sta
Page 253 and 254: 232 Dag Haugland and Di Yuan but no
Page 255 and 256: 234 Dag Haugland and Di Yuan [RAP-F
Page 257 and 258: 236 Dag Haugland and Di Yuan not fo
Page 259 and 260: 238 Dag Haugland and Di Yuan Table
Page 265 and 266: 244 Dag Haugland and Di Yuan for th
Page 267 and 268: 246 Dag Haugland and Di Yuan 39. Mo
Page 269 and 270: 248 Abdullah Konak, Orhan Dengiz, a
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Part IV Optimization Problems in th
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272 Marina Aguado, Jasone Astorga,
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Chapter 13 Optimization of Wireless
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13 Optimization of Wireless Broadba
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322 Nitin Salodkar and Abhay Karand
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354 Khiem Le People now expect to b
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356 Khiem Le a new paradigm is need
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358 Khiem Le of resources without r
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360 Khiem Le opportunities: locatio
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362 Khiem Le sufficient for mobile
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364 Khiem Le In this target archite
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366 Khiem Le to bootstrap the setup
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368 Khiem Le eliminates the OPEX tr
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370 Khiem Le results to the doctor
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372 Khiem Le http://www.3gamericas.
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