Cognitive radio network (CRN) provides an effective way of improving efficiency and flexibility in spectrum usage. Due to the coexistence of secondary user (SU) and primary user (PU), managing interference is a critical issue to be addressed if we are to reap the full benefits. In this paper, we consider the problem of joint interference management and resource allocation in a multi-channel ad hoc CRN. We formulate the problem as an overlapping coalition formation game to maximize the sum rate of SU links while guaranteeing the quality of service (QoS) of PU links. In the game, each SU link can make an autonomous decision and is allowed to participate in one or more cooperative coalitions simultaneously to maximize its payoff. To obtain the solution of the formulated game, a distributed, self-organizing algorithm is proposed for performing coalition formation. We analyze the properties of the algorithm and show that SU links can cooperate to reach a final stable coalition structure. Compared with existing approaches, the proposed scheme achieves appreciable performance improvement in terms of the sum rate of SU links, which is demonstrated by simulation results.

The demand response is attracting attention to perform electric power load leveling. In this paper, we consider a power consumption reduction problem with an aggregator that requests electric power consumption reduction to consumers by allocating a part of its profit to them as an incentive. We formulate interactions among consumers as a game, where the incentive to each consumer is determined by his/her contribution to the total power consumption reduction, and the consumer determines his/her own reduction amount selfishly to maximize his/her payoff. The uniqueness of best responses of each consumer and an equilibrium condition of the game are also derived. By using numerical simulations, we show relationship among incentive allocation rate, realized total reduction amount through the game, and the aggregator's payoff for the cases with the for-profit and the nonprofit aggregator.

In this paper, we study the power allocation problem in a relay assisted multi-band underlay cognitive radio network. Such a network allows unlicensed users (secondary users) to access the spectrum bands under a transmission power constraint. Due to the concave increasing property of logarithm function, it is not always wise for secondary users to expend all the transmission power in one band if their aim is to maximize achievable data rate. In particular, we study a scenario where two secondary users and a half-duplexing relay exist with two available bands. The two users choose different bands for direct data transmission and use the other band for relay transmission. By properly allocating the power on two bands, each user may be able to increase its total achievable data rate while satisfying the power constraint. We formulate the power allocation problem as a non-cooperative game and investigate its Nash equilibria. We prove the power allocation game is a supermodular game and that Nash equilibria exist. We further find the best response function of users and propose a best response update algorithm to solve the corresponding dynamic game. Numerical results show the overall performance in terms of achievable rates is improved through our proposed transmission scheme and power allocation algorithm. Our proposed algorithm also shows satisfactory performance in terms of convergence speed.

Coalition Structure Generation (CSG) is a main research issue in the domain of coalition games. A majority of existing works assume that the value of a coalition is independent of others in the coalition structure. Recently, there has been interest in a more realistic settings, where the value of a coalition is affected by the formation of other coalitions. This effect is known as externality. The focus of this paper is to make use of Maximum Satisfiability (MaxSAT) to solve the CSG problem where externalities may exist. In order to reduce the exponentially growing number of possible solutions in the CSG problem, we follow the previous works by representing the CSG problem as sets of rules in MC-nets (without externalities) and embedded MC-nets (with externalities). Specifically, enlightened by the previous MC-net-based algorithms exploiting the constraints among rule relations to solve the CSG problem, we encode such constraints into weighted partial MaxSAT (WPM) formulas. Experimental results demonstrate that an off-the-shelf MaxSAT solver achieves significant improvements compared to the previous algorithm for the same set of problem instances.

Coalition Structure Generation (CSG) means partitioning agents into exhaustive and disjoint coalitions so that the sum of values of all the coalitions is maximized. Solving this problem could be facilitated by employing some compact representation schemes, such as marginal contribution network (MC-net). In MC-net, the CSG problem is represented by a set of rules where each rule is associated with a real-valued weights, and the goal is to maximize the sum of weights of rules under some constraints. This naturally leads to a combinatorial optimization problem that could be solved with weighted partial MaxSAT (WPM). In general, WPM deals with only positive weights while the weights involved in a CSG problem could be either positive or negative. With this in mind, in this paper, we propose an extension of WPM to handle negative weights and take advantage of the extended WPM to solve the MC-net-based CSG problem. Specifically, we encode the relations between each pair of agents and reform the MC-net as a set of Boolean formulas. Thus, the CSG problem is encoded as an optimization problem for WPM solvers. Furthermore, we apply this agent relation-based WPM with minor revision to solve the extended CSG problem where the value of a coalition is affected by the formation of other coalitions, a coalition known as externality. Experiments demonstrate that, compared to the previous encoding, our proposed method speeds up the process of solving the CSG problem significantly, as it generates fewer number of Boolean variables and clauses that need to be examined by WPM solver.

In cognitive radio networks, the primary user (PU) can lease a fraction of its licensed spectrum to the secondary users (SUs) in exchange for their cooperative transmission if it has a minimum transmission rate requirement and is experiencing a bad channel condition. However, due to the selfish nature of the SUs, they may not cooperate to meet the PU's Quality of Service (QoS) requirement. On the other hand, the SUs may not exploit efficiently the benefit from cooperation if they compete with each other and collaborate with the PU independently. Therefore, when SUs belong to the same organization and can work as a group, how to stimulate them to cooperate with the PU and thus guarantee the PU's QoS requirement, and how to coordinate the usage of rewarded spectrum among these SUs after cooperation are critical challenges. In this paper, we propose a two-level bargaining framework to address the aforementioned problems. In the proposed framework, the interactions between the PU and the SUs are modeled as the upper level bargaining game while the lower level bargaining game is used to formulate the SUs' decision making process on spectrum sharing. We analyze the optimal actions of the users and derive the theoretic results for the one-PU one-SU scenario. To find the solutions for the one-PU multi-SU scenario, we put forward a revised numerical searching algorithm and prove its convergence. Finally, we demonstrate the effectiveness and efficiency of the proposed scheme through simulations.

We investigate the problem of joint frequency and power allocation in wireless mesh networks, using a self-pricing game based solution. In traditional pricing game models, the price factor is determined from the global information of the network, which causes heavy communication overhead. To overcome this problem, we propose a self-pricing game model, in which the price factor is determined by the distributed access points processing their individual information; moreover, it is implemented in an autonomous and distributed fashion. The existence and the efficiency of Nash equilibrium (NE) of the proposed game are studied. It is shown that the proposed game based solution achieves near cooperative network throughput while it reduces the communication overhead significantly. Also, a forcing convergence algorithm is proposed to counter the vibration of channel selection. Simulation results verify the effectiveness and robustness of the proposed scheme.

While efficient use of network resources is an important control objective of call admission control (CAC), the issue of fairness among services should also be taken into account. Game theory provides a suitable framework for formulating such fair and efficient CAC problem. Thus, in this paper, a game theoretic framework for selecting fair-efficient threshold parameters of CAC for the asymmetrical traffic case in CDMA mobile multimedia systems is proposed. For the cooperative game, the arbitration schemes for the interpersonal comparisons of utility and the bargaining problem, including the Nash, Raiffa, and modified Thomson solutions, are investigated. Furthermore, since CAC should be simple and flexible to provide a fast response to diverse QoS call requests during a connection setup, this paper also applies the concept of load factor to the previous Jeon and Jeong's CAC scheme and proposes an approximation approach to reduce the computational complexity (proposed throughput-based CAC scheme). From the numerical results, the proposed throughput-based CAC scheme shows a comparable performance to the previous Jeon and Jeong's CAC scheme while achieving lower computational complexity. All the solutions attain the fairness by satisfying their different fairness senses and efficiency by the Pareto optimality.

In this paper, we develop a new distributed adaptive power control framework for multi-cell OFDM systems based on the game theory. A specific utility function is defined considering the users' achieved average utility per power, i.e., power unit based utility. We solve the subcarrier allocation issue naturally as well as the power control. Each user tries to maximize its utility by adjusting the transmit power on each subcarrier. A Nash equilibrium for the game is shown to exist and the numerical results show that our proposal outperforms the pure water-filling algorithm in terms of efficiency and fairness.

Call admission control (CAC) plays a significant role in providing the efficient use of the limited bandwidth and the desired quality-of-service (QoS) in mobile multimedia communications. As efficiency is an important performance issue for CAC in the mobile networks with multimedia services, the concept of fairness among services should also be considered. Game theory provides an appropriate framework for formulating such fair and efficient CAC problem. Thus, in this paper, a framework based on game theory (both of noncooperative and cooperative games) is proposed to select fair-efficient guard bandwidth coefficients of the CAC scheme for the asymmetrical traffic case in mobile multimedia communications. The proposed game theoretic framework provides fairness and efficiency in the aspects of bandwidth utilization and QoS for multiple classes of traffic, and also guarantees the proper priority mechanism. Call classes are viewed as the players of a game. Utility function of the player is defined to be of two types, the bandwidth utilization and the weighted sum of new call accepting probability and handoff succeeding probability. The numerical results show that, for both types of the utility function, there is a unique equilibrium point of the noncooperative game for any given offered load. For the cooperative game, the arbitration schemes for the interpersonal comparisons of utility and the bargaining problem are investigated. The results also indicate that, for both types of the utility function, the Nash solution with the origin (0,0) as the starting point of the bargaining problem can achieve higher total utility than the previous CAC scheme while at the same time providing fairness by satisfying a set of fairness axioms. Since the Nash solution is determined from the domain of the Pareto boundary, the way to generate the Pareto boundary is also provided. Therefore, the Nash solution can be obtained easily.

In wireless communication systems where users compete for limited bandwidth, radio resource control is essential for throughput enhancement and delay reduction. In this paper, we present a game-theoretical approach to distributed resource control in CDMA systems. Incomplete information about channel conditions is considered. The resource control problem is formulated as a noncooperative game of incomplete information, with which the existence and uniqueness of the Bayesian Nash equilibrium (BNE) of the game is investigated. Since the equilibrium is Pareto inefficient, we propose a pricing policy to the resource control game by adding a penalty price to user's transmission cost. With the adoption of the price, user's aggressive behavior is depressed, and Pareto improvement is achieved. Also the Pareto efficient BNE of the game with pricing is studied. Simulation results show that users can obtain higher throughput and lower average packet transmission delay by proper pricing policy. It is also verified that the scheme of pricing policy is robust when information of channel conditions is inaccurate.