In this paper, a theoretical evaluation framework regarding the in wireless communication networks is introduced and examined. To study these equilibria operation points, we coin some new concepts, namely the , the and the , which can be used for measuring the efficiency of the obtained equilibria solutions. The aforementioned framework is analyzed and evaluated in a wireless communication environment under the presence of the Gaussian Interference channel (GIC). Within this setting, a non-cooperative game among the users is studied, where users aim in a selfish manner to meet their Quality of Service (QoS) prerequisite. However instead of maximizing the QoS which is generally energy costly, we evangelize that better energy-efficiency is achieved by targeting satisfactory QoS levels only. The sufficient and necessary conditions that lead to the are provided for the two-user case and the is determined, where the users satisfy their QoS constraints with the lowest possible cost. Moreover, specific measures for evaluating the efficiency of various satisfaction equilibria, in a formal and quantitative manner, expressing the tradeoff with respect to the achieved utility or a given objective function and corresponding cost, are defined and analyzed.
This paper addresses the problem of joint resource block (RB) and uplink transmission power allocation in a Device to Device (D2D) underlay cellular network via a game theoretic approach. In contrast to the majority of previous research works in this area, the proposed framework aims mainly at interference mitigation and energy efficiency. We do not restrict a priori the number of D2D pairs which can reuse a RB allocated to a cellular link. However, this is determined dynamically by the objective of interference minimization. To address the resource allocation problem under consideration and deal with its inherent complexity, a two-step distributed approach is proposed. At first the RB allocation process is modeled as an exact potential game which is shown to minimize the total interference in the network. Second a non-cooperative game theoretic model for the uplink transmission power allocation process is proposed. For both stages of the proposed framework, efficient and distributed algorithms for the computation of the desired Nash Equilibrium (NE) point of each game are introduced. The efficiency of the overall proposed framework is evaluated through modeling and simulation, while comparative numerical results are presented that demonstrate the superiority of our methodology against other state of the art approaches. So far, most equilibrium concepts in game theory require that the rewards and actions of the other agents are known and or observed by all agents. However, in real life problems, agents are generally faced with situations where they only have partial or no knowledge about their environment and the other agents evolving in it. In this context, all an agent can do is reasoning about its own payoffs and consequently, cannot rely on classical equilibria through deliberation, which requires full knowledge and observability of the other agents. To palliate to this difficulty, we introduce the satisfaction principle from which an equilibrium can arise as the result of the agents' individual learning experiences. We define such an equilibrium and then we present different algorithms that can be used to reach it. Finally, we present experimental results that show that using learning strategies based on this specific equilibrium, agents will generally coordinate themselves on a Pareto-optimal joint strategy, that is not always a Nash equilibrium, even though each agent is individually rational, in the sense that they try to maximize their own satisfaction.  SC-FDMA has been selected by the Third Generation Partnership Project (3GPP) Long Term Evolution (LTE) and LTE Advanced (LTE-A), as the primary multiple access scheme for the uplink of the 4?G wireless communication systems. This article surveys the different state-of-the-art resource allocation algorithms developed to allocate system's subcarriers and users' transmission powers in the uplink of SC-FDMA wireless networks. An objective-driven classification approach is followed where the various resource allocation algorithms are categorized into four major classes based on the ultimate goal objective of the resource allocation process, as follows: (i) throughput optimization, (ii) fairness, (iii) satisfaction of users' QoS requirements, and (iv) joint power and subcarriers allocation. The article summarizes the activities and recent advances on this work-in-progress area, highlights the key features, advantages and disadvantages of each algorithm, and provides a detailed qualitative comparative evaluation of their attributes and characteristics according to several distinct features. Furthermore, it provides some indicative numerical results that better present and quantify the objective and achieved performance of the various algorithms. Finally the problem of resource allocation is discussed, when multicell environment is considered, where issues such as intercell interference mitigation and backhaul rate constraints pose additional challenges.  This paper tackles the problem of joint users’ uplink transmission power and data rate allocation in multi-service two-tier femtocell networks. Each user—either macrocell (MUE) or femtocell user equipment (FUE)—is associated with a two-variable utility function that represents his perceived satisfaction with respect to his allocated resources (i.e., power and rate). User's utility function is differentiated based both on the tier that the user belongs to and the service he requests. The joint resource allocation problem is directly confronted as a two-variable optimization problem and formulated as a non-cooperative game. The theory of supermodular games is utilized towards treating the two-variable optimization problem and the inherent multidimensional competition that arises among the users. The existence of proposed game's Nash Equilibrium (NE) point is analytically shown, while game's convergence to its NE point is proven. A distributed and iterative algorithm for computing the desired NE is introduced, where the optimal values of each user's uplink transmission power and data rate are simultaneously updated at the same step. The performance of the proposed approach is evaluated via modeling and simulation and its superiority compared to other state of the art approaches is illustrated. This paper introduces a particular game formulation and its corresponding notion of equilibrium, namely the satisfaction form (SF) and the satisfaction equilibrium (SE). A game in SF models the case where players are uniquely interested in the satisfaction of some individual performance constraints, instead of individual performance optimization. Under this formulation, the notion of equilibrium corresponds to the situation where all players can simultaneously satisfy their individual constraints. The notion of SE, models the problem of QoS provisioning in decentralized self-configuring networks. Here, radio devices are satisfied if they are able to provide the requested QoS. Within this framework, the concept of SE is formalized for both pure and mixed strategies considering finite sets of players and actions. In both cases, sufficient conditions for the existence and uniqueness of the SE are presented. When multiple SE exist, we introduce the idea of effort or cost of satisfaction and we propose a refinement of the SE, namely the efficient SE (ESE). At the ESE, all players adopt the action which requires the lowest effort for satisfaction. A learning method that allows radio devices to achieve a SE in pure strategies in finite time and requiring only one-bit feedback is also presented. Finally, a power control game in the interference channel is used to highlight the advantages of modeling QoS problems following the notion of SE rather than other equilibrium concepts, e.g., generalized Nash equilibrium.