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A Markov chain S(t) is stable if the set of positive recurrent states is nonempty and contains a nite subset that is reached with probability 1 from any initial state [71]. In this context the term stability implies the existence of a stationary probability distribution. In addition, if all positive recurrent states are connected, the stationary distribution is unique.

6.7.1 Multiuser Physical Layer Model with Imperfect CSIT 6.7.2 System Utility Function 248 6.7.3 The Scheduling Problem Formulation 249 6.7.4 The Optimal Scheduling Solution 249 6.7.5 Suboptimal Solution 251 6.7.6 Numerical Results 253 6.8 Summary 253 Appendix 6A: Proof of Lemma 6.2 257 Appendix 6B: Calculation of Weights 257 Appendix 6C: Proof of Lemma 6.3 258 Appendix 6D: Proof of Lemma 6.4 259 Appendix 6E: Proof of Lemma 1.10 259 Appendix 6F: Proof of Lemma 6.5 259 Appendix 6G: Optimal Power Allocation for Network Capacity Utility 261 Appendix 6H: Asymptotic Distribution of Sk 262 Exercises 264

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Learning Perspective How do we continue to learn and improve our ability to accomplish our goals Brainstorm a large list of criteria under each of the four perspectives Then review the lists and select four to eight of the most important performance criteria If you can position IT to deliver capabilities that meet these criteria, the IT organization has accomplished its mission For instance, the CIO may define financial criteria such as build the new systems for X dollars and be able to operate them for Y dollars per year Customer measures could track activities that provide customers with Web-based capabilities to do a range of activities such as look up products, enter orders, and pay bills.

7 Review of MAC Layer Design for Wireless Systems 269 7.1 Overview 269 7.2 TDMA-Based MAC Layer Design 272 7.2.1 ALOHA 272 7.2.2 Slotted ALOHA 272 7.2.3 PRMA 273 7.2.4 TDMA with Request Subframe 273 7.3 CDMA-Based MAC Layer Design 275 7.4 Issues in Request Scheduling 277 7.5 Design Example a Jointly Adaptive TDMA MAC Protocol 279 7.5.1 Overview of TDMA MAC Protocol Design 279 7.5.2 Synergistic Channel-Adaptive Multiple Access 280 7.6 Design Example a Jointly Adaptive CDMA MAC Protocol 290 7.6.1 Channel Adaptation in CDMA Systems 290 7.6.2 Optimal and Near-Optimal Approaches 291 7.6.3 Approaches that Maintain Fairness 293 7.6.4 User-Oriented Heuristics 294 7.7 Performance Comparisons 295 7.8 Summary 303 Exercises 303

8 Channel-Adaptive Wireless Fair Scheduling 8.1 Overview 307 8.2 Scheduling Model 308 8.2.1 Fluid Fair Queueing 308 8.2.2 Service Model 308 8.2.3 A Motivating Example 309 8.3 Design Framework 310 8.3.1 Error-Free Service Model 310 8.3.2 Lead Lag Model 310 8.3.3 Compensation Model 311 8.3.4 Slot Queue and Packet Queue 312 8.4 Fairness Notions 312 8.4.1 Effort Fair and Outcome Fair 312 8.4.2 Fairness Notions for Wireline Networks 313 8.4.3 Fairness Notions for Wireless Networks 314 8.4.4 Channel-Adaptive Fairness 318 8.4.5 Comparison with Other Fairness Notions 319 8.5 Wireless Packet Scheduling Algorithms 319 8.5.1 Idealized Wireless Fair Queueing (IWFQ) 319 8.5.2 Channel-Condition-Independent Fair Queueing (CIFQ) 8.5.3 Server-Based Fairness Approach (SBFA) 320 8.5.4 Wireless Fair Service (WFS) 321 8.5.5 Utility-Based Opportunistic Scheduling 321 8.5.6 Channel-Adaptive Fair Queueing 322 8.6 Numerical Examples 328 8.6.1 Parameters 328 8.6.2 Scenario 1 329 8.6.3 Scenario 2 331 8.6.4 Scenario 3 332 8.6.5 Scenario 4 332 8.7 Channel-Adaptive Scheduling for CDMA Systems 334 8.7.1 Code Allocation in Multicode CDMA Systems 335 8.7.2 CAFQ for CDMA Systems 335 8.8 Summary 337 Exercises 338 9 Packet-Switched Data Services in W-CDMA Systems 9.1 Overview 341 9.2 UMTS Architecture 343 9.2.1 Radio Interface 345 9.2.2 PHY Layer 347

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