Stackelberg Bayesian Game for Power Allocation in Two-Tier Networks

Gao

Trans Emerging Tel Tech

et al. 2017

We consider mobile crowdsensing campaigns that are practiced in the emerging data collection in femtocell networks, which are effective in improving the capacity and coverage of communication users. One of the challenging issues is how to allocate the transmission power and reduce the cross‐tier and co‐tier interferences among users. This paper proposes a Stackelberg game to characterize the actions of macrocell and femtocell users. In the game, the macrocell user is seen as the leader, whereas the femtocell users are seen as the followers. For the macrocell, it aims at maximizing the leader's profit with the constraint of the maximum tolerable interference. The femtocell users attempt to maximize the followers' profit in the network system with consideration of performance guarantee in the aspects of packet delivery rate and time delay, which are modeled as probability constraints. Spectrum sharing is assumed among the macrocell and femtocells. The uncertain channel state information is considered in the formulated problem, and the probability threshold method is utilized to convert the uncertain constraints into tractable ones. Furthermore, a power control and price bargaining algorithm is derived to get the practical operation. Finally, numerical results validate the performances of the proposed scheme in the aspects of convergence, utility of uses, and power consumption.

Section: Definition 1 Let C *mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Distributed power control in crowdsensing‐based femtocell networks with QoS provisioning

Gao

Trans Emerging Tel Tech

et al. 2017

IEEE Trans. Cogn. Commun. Netw.

“…In effect therefore, the leaderfollower relationship in a Stackelberg game is quite similar to our landlord-tenant interpretation in a spectrum supply chain network. The transmit power allocation problem has been formulated as a two-tier Stackelberg game for both cellular [48] and cognitive radio networks [30]. Extensions to multilevels of hierarchy in decision making, with many leaders and followers similar to Figure 3 is also possible [47].…”

Section: Cognitive Radio Network Viewed As Spectrum Supply Chainmentioning

confidence: 99%

Cognitive Radio Networks: The Spectrum Supply Chain Paradigm

Haykin

Setoodeh

2015

Cognitive radio provides a basis for addressing the practical issue of spectrum scarcity. This issue has been raised due to the continuing advances in wireless technology, which has led to ever-increasing demand for larger bandwidth. The issue of spectrum scarcity has been exacerbated due to inefficient use of the electromagnetic spectrum. Adopting the novel idea of cognitive radio for secondary usage of underutilized spectrum results in the existence of two worlds of wireless communications going on side by side: the legacy wireless world and the cognitive wireless world. Spectrum holes (i.e., the unused spectrum subbands) are the medium through which these two worlds dynamically interact. Releasing subbands by primary users allows the cognitive radio users to sustain communication and perform their normal tasks. Combination of the two wireless worlds can be viewed as a spectrum supply chain network, in which the legacy owners and their customers (primary users) play the role of the suppliers and cognitive radios (secondary users) play the role of consumers. This paper discusses two classes of spectrum supply chain networks based on two regimes, one allows open-access to the spectrum, and the other is a marketdriven regime. Each one of them has its own merits and suitability for a different environment; therefore, they have complementary roles. Analytic models are developed for these two classes of networks, which allow for analysis of both equilibrium and transient behaviors.

IEEE Trans. Aerosp. Electron. Syst.

“…The increasing need for independent, autonomous and decentralized communication systems has sparked much interest in using game theoretic techniques in the communication literature [7]. More specifically, the aforementioned distributed, multistatic beamforming and resource allocation problem in radar systems can be compared to similar issues raised in multicell wireless systems in communication applications [8]- [16]. In [8], the authors introduced the idea of joint beamforming and power control, proposing an iterative algorithm to simultaneously obtain the optimal beamforming and power vectors.…”

Section: Introductionmentioning

confidence: 99%

Game theoretic analysis for MIMO radars with multiple targets

Deligiannis

Lambotharan

Chambers

2016

This paper considers a distributed beamforming and resource allocation technique for a radar system in the presence of multiple targets. The primary objective of each radar is to minimize its transmission power while attaining an optimal beamforming strategy and satisfying a certain detection criterion for each of the targets. Therefore, we use convex optimization methods together with noncooperative and partially cooperative game theoretic approaches. Initially, we consider a strategic noncooperative game (SNG), where there is no communication between the various radars of the system. Hence each radar selfishly determines its optimal beamforming and power allocation. Subsequently, we assume a more coordinated game theoretic approach incorporating a pricing mechanism. Introducing a price in the utility function of each radar/player, enforces beamformers to minimize the interference induced to other radars and to increase the social fairness of the system. Furthermore, we formulate a Stackelberg game by adding a surveillance radar to the system model, which will play the role of the leader, and hence the remaining radars will be the followers. The leader applies a pricing policy of interference charged to the followers aiming at maximizing his profit while keeping the incoming interference under a certain threshold. We also present a proof of the existence and uniqueness of the Nash Equilibrium (NE) in both the partially cooperative and noncooperative games. Finally, the simulation results confirm the convergence of the algorithm in all three cases.