Femtocells are receiving considerable interest in mobile communications as a strategy to overcome the indoor coverage problems as well as to improve the efficiency of current macrocell systems. One of the most critical issues in femtocells is the potential interference between nearby femtocells and from femtocells to macrocells or to mobile handsets. In this work, we illustrate some decentralized strategies for an OFDMA femtocell system, based on game theory, where non-cooperative femtocells self-organize in order to find out the most appropriate access strategy, considering the decision phase and radio access jointly. The strategies illustrated in this work fall within the context of the FREEDOM European Project
One of the most critical issues in femtocell network deployment is interference management, especially for femtocells sharing the spectrum occupied by conventional cellular networks. In this work we propose and analyze an optimal power allocation strategy based on modeling the interferer's activity as a two-state Markov chain. In the single femto-user access, we show how to maximize the expected value of femto-user rate, averaged over the interference statistical model. Then, we extend the approach to the multiuser case, adopting a game-theoretic formulation to devise decentralized access strategies, particularly suitable in view of potential massive deployment of femto access points
Femtocell networks offer a series of advantages with respect to conventional cellular networks. However, a potential massive deployment of femto-access points (FAPs) poses a big challenge in terms of interference management, which requires proper radio resource allocation techniques. In this article, we propose alternative optimal power/bit allocation strategies over a time-frequency frame based on a statistical modeling of the interference activity. Given the lack of knowledge of the interference activity, we assume a Bayesian approach that provides the optimal allocation, conditioned to periodic spectrum sensing, and estimation of the interference activity statistical parameters. We consider first a single FAP accessing the radio channel in the presence of a dynamical interference environment. Then, we extend the formulation to a multi-FAP scenario, where nearby FAP's react to the strategies of the other FAP's, still within a dynamical interference scenario. The multi-user case is first approached using a strategic non-cooperative game formulation. Then, we propose a coordination game based on the introduction of a pricing mechanism that exploits the backhaul link to enable the exchange of parameters (prices) among FAP's.
In this paper we address the problem of distributed sum-rate maximization in small cell networks in the special case where users have incomplete information about the interfering cross-channels. The sum-rate maximization problem admits a decentralized solution based on pricing mechanism. This requires the exchange of some control parameters (prices) over a common signaling channel. In femtocell networks, this exchange of control data may occur through the wired backhaul (e.g., an ADSL channel) connecting the femto-access points (FAP's). The problem with the implementation of pricing is that it requires knowledge of the cross channels between the interfering nodes and this information typically is not available. In this paper, we address this problem by using only statistical information about the cross-channels. This leads to a Bayesian pricing mechanism. We start modeling the interference mechanism through a directed random graph. Then, we model the interference incorporating the fading statistics and the spatial distribution of the nodes. We provide a closed form expression for the expected value of the pricing term and derive distributed schemes able to converge to a stationary point of the sum-utility function. Finally, we compare the proposed approach to a purely competitive mechanism and to a clairvoyant (gene-aided) scheme
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