Wireless Network Design: Optimization Models and Solution ...

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13 Optimization of Wireless Broadband (WiMAX) Systems 317 Table 13.4 Distribution of Users Channel Conditions Table 13.5 User Traffic QoS Distributions Table 13.6 Systems Simulation Results Mobility Case Fading Channel Model Percentage Low mobility ITU Ped A, 3km/H 40% Low mobility ITU Ped B, 3km/H 40% High mobility ITU Veh A, 90km/H 20% Application Traffic Category Percentage FTP Best effort 30% Web browser/HTTP Interactive/real time 50% VOIP Real time 10% Gaming Interactive real time 10% DL systems throughput 17.7 Mbps UL systems throughput 3.92Mbps DL spectrum efficiency 2.66 UL spectrum efficiency 1.18 DL packet Delay (90% percentile) VOIP 7ms Gaming 17ms FTP 72s (average download time) Web 2.5s UL packet Delay (90% percentile) VOIP 5ms Gaming 10ms FTP 52s (average upload time) In this experiment, the distribution of user channel condition and the distribution of traffic QoS still follow the distribution listed in table 4 and 5, respectively. Additionally, multiple service levels are defined as shown in Table 13.7. Table 13.7 Distribution of Service Levels Service Level Max. Service Rate(Mbps) Sustain Rate (Kbps) Percentage Diamond Level DL: 1/UL: 0.5 DL: 20/UL: 10 30% Gold Level DL: 0.5 /UL: 0.25 DL: 20/UL: 10 40% Silver Level DL: 0.25/UL: 0.128 DL: 20/UL: 10 30% Each service level is defined by one max. service rate and one sustain rate. Each user is allocated one service level. The user throughput should never exceed the max. service rate, and the sustain rate is required for each CPE to maintain the wireless connection. The service level is realized through the radio resource allocation scheme. In this experiment, the policy for radio resource allocation is to:
318 Hang Jin and Li Guo 1. Guarantee the packet delay for users with real-time service: VoIP and gaming 2. Guarantee the throughput of FTP and Web users to be within the max. service rate and sustain rate defined by the service level of each individual user. So the basic procedure of the radio resource allocation is: 1. First allocate radio resource to Gaming and VoIP users to ensure the packet delay requirement; 2. Allocated radio resource to FTP and Web users, whose user throughput is less than the sustain rate defined in his service level; 3. Finally, allocate the remaining resource to those users whose throughput is lesser than the max. service rate but larger than the sustain rate. In this experiment, 200 active users are simulated. The fairness metric defined in IEEE 802.16j [10] is employed in the experiment. In IEEE 802.16j, the fairness is defined as the reciprocal of the standard deviation of the normalized throughput with the mean throughput ¯x = 1 K F = K ∑ k=1 � K K ∑ ( k=1 xk − 1)2 ¯x xk . This metric grows with larger fairness, and is not bounded. The output of the experiment shows that the fairness of 200 users is 1.53, which is slightly better than Fair-Time scheduler. From the above experiments, we can see that through proper design of radio resource allocation scheme in WiMax system, we can satisfy the various performance requirements. 13.6 Conclusion In this chapter, we have discussed the optimization problem for radio resource management in OFDMA-based WiMax systems. Radio resource allocation in WiMax is a classic optimization problem with multiple factors. Various optimization solutions exist with different metrics and tradeoff between complexity and performance exist among those solutions. The dynamic features in most of the aspects of OFDMAbased WiMax systems impose difficulty on the optimization solution of a radio resource allocation problem. A Particular performance metric and requirement needs to be considered in the design of each radio resource allocation algorithm.
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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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Chapter 11 Improving Network Connec
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15 Major Trends in Cellular Network
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