5 research outputs found
Automated Dynamic Offset Applied to Cell Association
In this paper, we develop a hierarchical Bayesian game framework for
automated dynamic offset selection. Users compete to maximize their throughput
by picking the best locally serving radio access network (RAN) with respect to
their own measurement, their demand and a partial statistical channel state
information (CSI) of other users. In particular, we investigate the properties
of a Stackelberg game, in which the base station is a player on its own. We
derive analytically the utilities related to the channel quality perceived by
users to obtain the equilibria. We study the Price of Anarchy (PoA) of such
system, where the PoA is the ratio of the social welfare attained when a
network planner chooses policies to maximize social welfare versus the social
welfare attained in Nash/Stackeleberg equilibrium when users choose their
policies strategically. We show by means of a Stackelberg formulation, how the
operator, by sending appropriate information about the state of the channel,
can configure a dynamic offset that optimizes its global utility while users
maximize their individual utilities. The proposed hierarchical decision
approach for wireless networks can reach a good trade-off between the global
network performance at the equilibrium and the requested amount of signaling.
Typically, it is shown that when the network goal is orthogonal to user's goal,
this can lead the users to a misleading association problem.Comment: 12 pages, 3 figures, technical report. arXiv admin note: text overlap
with arXiv:1002.3931, arXiv:0903.2966 by other author
Automated Dynamic Offset Applied to Cell Association
International audienceIn this paper, we develop a hierarchical Bayesian game framework for automated dynamic offset selection. Users compete to maximize their throughput by picking the best locally serving radio access network (RAN) with respect to their own measurement, their demand and a partial statistical channel state information (CSI) of other users. In particular, we investigate the properties of a Stackelberg game, in which the base station is a player on its own. We derive analytically the utilities related to the channel quality perceived by users to obtain the equilibria. We study the Price of Anarchy (PoA) of such system, where the PoA is the ratio of the social welfare attained when a network planner chooses policies to maximize social welfare versus the social welfare attained in Nash/Stackeleberg equilibrium when users choose their policies strategically
An Automated Dynamic Offset for Network Selection in Heterogeneous Networks
IEEE Early Access ArticlesInternational audience—Complementing traditional cellular networks with the option of integrated small cells and WiFi access points can be used to further boost the overall traffic capacity and service level. Small cells along with WiFi access points are projected to carry over 60% of all the global data traffic by 2015. With the integration of small cells on the radio access network levels, there is a focus on providing operators with more control over small cell selection while reducing the feedback burden. Altogether, these issues motivate the need for innovative distributed and autonomous association policies that operate on each user under the network operator's control, utilizing only partial information, yet achieving near-optimal solutions for the network. In this paper, we propose a load-aware network selection approach applied to automated dynamic offset in heterogeneous networks (HetNets). In particular, we investigate the properties of a hierarchical (Stackelberg) Bayesian game framework, in which the macro cell dynamically chooses the offset about the state of the channel in order to guide users to perform intelligent network selection decisions between macro cell and small cell networks. We derive analytically the utility related to the channel quality perceived by users to obtain the equilibria, and compare it to the fully centralized (optimal), the full channel state information and the non-cooperative (autonomous) models. Building upon these results, we effectively address the problem of how to intelligently configure a dynamic offset which optimizes network's global utility while users maximize their individual utilities. One of the technical contributions of the paper lies in obtaining explicit characterizations of the dynamic offset at the equilibrium and the related performances in terms of the price of anarchy. Interestingly, it turns out that the complexity of the algorithm for finding the dynamic offset of the Stackelberg model is O(n 4) (where n is the number of users). It is shown that the proposed hierarchical mechanism keeps the price of anarchy almost equal to 1 even for a low number of users, and remains bounded above by the non-cooperative model