7 research outputs found
Random Access Game in Fading Channels with Capture: Equilibria and Braess-like Paradoxes
The Nash equilibrium point of the transmission probabilities in a slotted
ALOHA system with selfish nodes is analyzed. The system consists of a finite
number of heterogeneous nodes, each trying to minimize its average transmission
probability (or power investment) selfishly while meeting its average
throughput demand over the shared wireless channel to a common base station
(BS). We use a game-theoretic approach to analyze the network under two
reception models: one is called power capture, the other is called signal to
interference plus noise ratio (SINR) capture. It is shown that, in some
situations, Braess-like paradoxes may occur. That is, the performance of the
system may become worse instead of better when channel state information (CSI)
is available at the selfish nodes. In particular, for homogeneous nodes, we
analytically present that Braess-like paradoxes occur in the power capture
model, and in the SINR capture model with the capture ratio larger than one and
the noise to signal ratio sufficiently small.Comment: 30 pages, 5 figure
Distributed Game Theoretic Optimization and Management of Multichannel ALOHA Networks
The problem of distributed rate maximization in multi-channel ALOHA networks
is considered. First, we study the problem of constrained distributed rate
maximization, where user rates are subject to total transmission probability
constraints. We propose a best-response algorithm, where each user updates its
strategy to increase its rate according to the channel state information and
the current channel utilization. We prove the convergence of the algorithm to a
Nash equilibrium in both homogeneous and heterogeneous networks using the
theory of potential games. The performance of the best-response dynamic is
analyzed and compared to a simple transmission scheme, where users transmit
over the channel with the highest collision-free utility. Then, we consider the
case where users are not restricted by transmission probability constraints.
Distributed rate maximization under uncertainty is considered to achieve both
efficiency and fairness among users. We propose a distributed scheme where
users adjust their transmission probability to maximize their rates according
to the current network state, while maintaining the desired load on the
channels. We show that our approach plays an important role in achieving the
Nash bargaining solution among users. Sequential and parallel algorithms are
proposed to achieve the target solution in a distributed manner. The
efficiencies of the algorithms are demonstrated through both theoretical and
simulation results.Comment: 34 pages, 6 figures, accepted for publication in the IEEE/ACM
Transactions on Networking, part of this work was presented at IEEE CAMSAP
201
Thwarting Selfish Behavior in 802.11 WLANs
The 802.11e standard enables user configuration of several MAC parameters,
making WLANs vulnerable to users that selfishly configure these parameters to
gain throughput. In this paper we propose a novel distributed algorithm to
thwart such selfish behavior. The key idea of the algorithm is for honest
stations to react, upon detecting a selfish station, by using a more aggressive
configuration that penalizes this station. We show that the proposed algorithm
guarantees global stability while providing good response times. By conducting
a game theoretic analysis of the algorithm based on repeated games, we also
show its effectiveness against selfish stations. Simulation results confirm
that the proposed algorithm optimizes throughput performance while discouraging
selfish behavior. We also present an experimental prototype of the proposed
algorithm demonstrating that it can be implemented on commodity hardware.Comment: 14 pages, 7 figures, journa