ABSTRACT
Contents 9.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 260 9.2 Formulation of the DSA Problem . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 262
9.2.1 DSA in Multiple Access Channels. . . . . . . . . . . . . . . . . . . . . . . . . . . 263 9.2.2 DSA in Interference Channels . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 264 9.2.3 General Assumptions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 265
9.3 Dynamic Spectrum Access as a Game . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 265 9.3.1 The Game Model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 265 9.3.2 Noncooperative and Cooperative Games . . . . . . . . . . . . . . . . . . . 266 9.3.3 The Nash Equilibrium Concept . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 266 9.3.4 Optimality Measures . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 267
9.4 Open Spectrum Access Games . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 267 9.4.1 Formulation of the Game . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 268
9.4.1.1 The Choice of the Utility Function. . . . . . . . . . . . . . . . 268
9.4.2 Single-Stage Games . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 270 9.4.2.1 MAC Single-Stage Games . . . . . . . . . . . . . . . . . . . . . . . . . 270 9.4.2.2 IC Single-Stage Games . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 273
9.4.3 Repeated Games . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 274 9.4.4 Bayesian Games . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 277 9.4.5 Coalitional Games . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 279
9.5 Hierarchical Spectrum Access Games . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 281 9.5.1 Stackelberg Games . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 281
9.6 Concluding Remarks . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 285 References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 286
In this chapter, the competitive interaction of radio devices dynamically accessing the spectrum is studied using tools from game theory. Depending on the scenario under consideration, the dynamic spectrum access (DSA) is modeled by different types of games following both a noncooperative and a cooperative approach. In the first case, each radio device aims to selfishly maximize an individual performance metric (e.g., individual data rate), while in the second case, such maximization concerns global network parameters (e.g., network sum-rate). In each case, we analyze network equilibria that allow network designers, operators, or manufactures to predict the behavior and the performance of cognitive networks or terminals.
