In this paper we study a transmission power tune problem with densely deployed 802.11 Wireless Local Area Networks (WLANs). While previous papers emphasize on tuning transmission power with either PHY or MAC layer separately, optimally setting each Access Point's (AP's) transmission power of a densely deployed 802.11 network considering its dual effects on both layers remains an open problem. In this work, we design a measure by evaluating impacts of transmission power on network performance on both PHY and MAC layers. We show that such an optimization problem is intractable and then we investigate and develop an analytical framework to allow simple yet efficient solutions. Through simulations and numerical results, we observe clear benefits of the dual-effect model compared to solutions optimizing solely on a single layer; therefore, we conclude that tuning transmission power from a dual layer (PHY-MAC) point of view is essential and necessary for dense WLANs. We further form a game theoretical framework and investigate above power-tune problem in a strategic network. We show that with decentralized and strategic users, the Nash Equilibrium (N.E.) of the corresponding game is in-efficient and thereafter we propose a punishment based mechanism to enforce users to adopt the social optimal strategy profile under both perfect and imperfect sensing environments.
More recently, power control problems have been analyzed under game theoretical framework. proposed a game theoretical approach for decentralized power allocation in @cite_0 . In @cite_1 , a congestion game model is proposed to analyze power control problem as a form of resource allocation. Equilibrium strategies have been given under certain assumptions. In @cite_5 , a power control problem is modeled as repeated games with strategic users and intervention theory is proposed to induce target strategy from users. Imperfect monitoring repeated game model is analyzed in @cite_6 with the assumption of a Local Spectrum Server (LSS). In @cite_2 , consider a power control problem w.r.t. reducing contention order on the link layer while keeping the physical layer interference under certain levels.
@cite_1 @cite_6 @cite_0 @cite_2 @cite_5
2540490069 1981109871 2044772040 2531737155
In this paper, we study resource allocation for a multicarrier-based cognitive radio (CR) network. More specifically, we investigate the problem of secondary users’ energy-efficiency (EE) maximization problem under secondary total power and primary interference constraints. First, assuming cooperation among the secondary base stations (BSs), a centralized approach is considered to solve the EE optimization problem for the CR network where the primary and secondary users are using either orthogonal frequency-division multiplexing (OFDM) or filter bank based multicarrier (FBMC) modulations. We propose an alternating-based approach to solve the joint power-subcarrier allocation problem. More importantly, in the first place, subcarriers are allocated using a heuristic method for a given feasible power allocation. Then, we conservatively approximate the nonconvex power control problem and propose a joint successive convex approximation-Dinkelbach algorithm (SCADA) to efficiently obtain a solution to the nonconvex power control problem. The proposed algorithm is shown to converge to a solution that coincides with the stationary point of the original nonconvex power control subproblem. Moreover, we propose a dual decomposition-based decentralized version of the SCADA. Second, under the assumption of no cooperation among the secondary BSs, we propose a fully distributed power control algorithm from the perspective of game theory. The proposed algorithm is shown to converge to a Nash-equilibrium (NE) point. Moreover, we propose a sufficient condition that guarantees the uniqueness of the achieved NE. Extensive simulation analyses are further provided to highlight the advantages and demonstrate the efficiency of our proposed schemes. This paper proposes a decentralized interference control scheme for an OFDMA (Orthogonal Frequency Division Multiple Access) cellular scenario where multiple macrocells and femtocells are deployed. In particular, we model the distributed resource allocation problem by means of a potential game, which is demonstrated to converge to a unique and pure Nash equilibrum. In this game, the femto base stations are the players, their actions are the resource blocks and the corresponding power levels to be allocated for downlink transmission, and the utility is designed to guarantee coexistence with the macro system, guaranteeing a fair tradeoff between femto and macro systems' performances. To do that, the utility function does not only consider the capacity of the femtocells, but also the different sources of inter-system interference: macrocell to femtocell, femtocell to femtocell, and femtocell to macrocell. The game is solved by applying a better response dynamic, which first selects the best resource blocks allocation policy for the femtousers, and then selects the most appropriate power levels for each resource block, through the solution of the corresponding convex optimization problem. Simulation results show that the introduction of femtocells in the scenario increases the total system capacity, and that the considered utility function provides an improvement in macro system performance of up to 40 , while reducing the femto capacity only up to 7 , with respect to the well known iterative waterfilling game. We propose a new distributed power control scheme for closed access femtocell networks. Since femto APs can be deployed unpredictably in a macro cell area, interference to macro users and interference between femto APs in down-link (DL) cause a severe problem. In this paper, we describe the system model using a noncooperative game model. We formulate a payoff function to provide fairness and to minimize interference. At the same time, we prove that this function can be appeared as a supermodular type. Our power control algorithm based on the game model can be applied to the decentralized environment. This novel power control algorithm leads transmission power to reach a steady state condition, i.e. Nash Equilibrium (NE). Through mathematical analysis and numerical results, we show that our proposed power control algorithm has several good characteristics. 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.