15 research outputs found
Parallel computing as a congestion game
Game-theoretical approach to the analysis of parallel algorithms is proposed.
The approach is based on presentation of the parallel computing as a congestion
game. In the game processes compete for resources such as core of a central
processing unit and a communication subsystem. There are players, resources and
payoffs (time delays) of players which depend on resources usage. Comparative
analysis of various optimality principles in the proposed model may be
performed
Distributed Spectrum Access for Cognitive Small Cell Networks: A Robust Graphical Game Approach
This letter investigates the problem of distributed spectrum access for
cognitive small cell networks. Compared with existing work, two inherent
features are considered: i) the transmission of a cognitive small cell base
station only interferes with its neighbors due to the low power, i.e., the
interference is local, and ii) the channel state is time-varying due to fading.
We formulate the problem as a robust graphical game, and prove that it is an
ordinal potential game which has at least one pure strategy Nash equilibrium
(NE). Also, the lower throughput bound of NE solutions is analytically
obtained. To cope with the dynamic and incomplete information constraints, we
propose a distribute spectrum access algorithm to converge to some stable
results. Simulation results validate the effectiveness of the proposed
game-theoretic distributed learning solution in time-varying spectrum
environment.Comment: 7 pages, 5 figures, Submitted to IEEE Transactions on Vehicular
Technology as a correspondenc
Load-aware Dynamic Spectrum Access for Small Cell Networks: A Graphical Game Approach
In this letter, we investigate the problem of dynamic spectrum access for
small cell networks, using a graphical game approach. Compared with existing
studies, we take the features of different cell loads and local interference
relationship into account. It is proved that the formulated spectrum access
game is an exact potential game with the aggregate interference level as the
potential function, and Nash equilibrium (NE) of the game corresponds to the
global or local optima of the original optimization problem. A lower bound of
the achievable aggregate interference level is rigorously derived. Finally, we
propose an autonomous best response learning algorithm to converge towards its
NE. It is shown that the proposed game-theoretic solution converges rapidly and
its achievable performance is close to the optimum solution.Comment: 6 pages, 5 figures, Submitted to IEEE Transactions on Vehicular
Technology as a correspondence (under third round review
Distributed Nash Equilibrium Seeking by A Consensus Based Approach
In this paper, Nash equilibrium seeking among a network of players is
considered. Different from many existing works on Nash equilibrium seeking in
non-cooperative games, the players considered in this paper cannot directly
observe the actions of the players who are not their neighbors. Instead, the
players are supposed to be capable of communicating with each other via an
undirected and connected communication graph. By a synthesis of a
leader-following consensus protocol and the gradient play, a distributed Nash
equilibrium seeking strategy is proposed for the non-cooperative games.
Analytical analysis on the convergence of the players' actions to the Nash
equilibrium is conducted via Lyapunov stability analysis. For games with
non-quadratic payoffs, where multiple isolated Nash equilibria may coexist in
the game, a local convergence result is derived under certain conditions. Then,
a stronger condition is provided to derive a non-local convergence result for
the non-quadratic games. For quadratic games, it is shown that the proposed
seeking strategy enables the players' actions to converge to the Nash
equilibrium globally under the given conditions. Numerical examples are
provided to verify the effectiveness of the proposed seeking strategy
A Unified Strategy for Solution Seeking in Graphical N-coalition Noncooperative Games
This paper aims to reduce the communication and computation costs of the Nash
equilibrium seeking strategy for the -coalition noncooperative games
proposed in [1]. The objective is achieved in two manners: 1. An interference
graph is introduced to describe the interactions among the agents in each
coalition. 2. The Nash equilibrium seeking strategy is designed with the
interference graphs considered. The convergence property of the proposed Nash
equilibrium seeking strategy is analytically investigated. It is shown that the
agents' actions generated by the proposed method converge to a neighborhood of
the Nash equilibrium of the graphical -coalition noncooperative games under
certain conditions. Several special cases where there is only one coalition
and/or there are coalitions with only one agent are considered. The results for
the special cases demonstrate that the proposed seeking strategy achieves the
solution seeking for noncooperative games, social cost minimization problems
and single-agent optimization problems in a unified framework. Numerical
examples are presented to support the theoretical results
Revisiting Optimal Power Control: its Dual Effect on SNR and Contention
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
Approximate Best-Response Dynamics in Random Interference Games
In this paper we develop a novel approach to the convergence of Best-Response
Dynamics for the family of interference games. Interference games represent the
fundamental resource allocation conflict between users of the radio spectrum.
In contrast to congestion games, interference games are generally not potential
games. Therefore, proving the convergence of the best-response dynamics to a
Nash equilibrium in these games requires new techniques. We suggest a model for
random interference games, based on the long term fading governed by the
players' geometry. Our goal is to prove convergence of the approximate
best-response dynamics with high probability with respect to the randomized
game. We embrace the asynchronous model in which the acting player is chosen at
each stage at random. In our approximate best-response dynamics, the action of
a deviating player is chosen at random among all the approximately best ones.
We show that with high probability, with respect to the players' geometry and
asymptotically with the number of players, each action increases the expected
social-welfare (sum of achievable rates). Hence, the induced sum-rate process
is a submartingale. Based on the Martingale Convergence Theorem, we prove
convergence of the strategy profile to an approximate Nash equilibrium with
good performance for asymptotically almost all interference games. We use the
Markovity of the induced sum-rate process to provide probabilistic bounds on
the convergence time. Finally, we demonstrate our results in simulated
examples
Distributed Spectrum Access with Spatial Reuse
Efficient distributed spectrum sharing mechanism is crucial for improving the
spectrum utilization. The spatial aspect of spectrum sharing, however, is less
understood than many other aspects. In this paper, we generalize a recently
proposed spatial congestion game framework to design efficient distributed
spectrum access mechanisms with spatial reuse. We first propose a spatial
channel selection game to model the distributed channel selection problem with
fixed user locations. We show that the game is a potential game, and develop a
distributed learning mechanism that converges to a Nash equilibrium only based
on users' local observations. We then formulate the joint channel and location
selection problem as a spatial channel selection and mobility game, and show
that it is also a potential game. We next propose a distributed strategic
mobility algorithm, jointly with the distributed learning mechanism, that can
converge to a Nash equilibrium
Low-Complexity Distributed Radio Access Network Slicing: Algorithms and Experimental Results
Radio access network (RAN) slicing is an effective methodology to dynamically
allocate networking resources in 5G networks. One of the main challenges of RAN
slicing is that it is provably an NP-Hard problem. For this reason, we design
near-optimal low-complexity distributed RAN slicing algorithms. First, we model
the slicing problem as a congestion game, and demonstrate that such game admits
a unique Nash equilibrium (NE). Then, we evaluate the Price of Anarchy (PoA) of
the NE, i.e., the efficiency of the NE as compared to the social optimum, and
demonstrate that the PoA is upper-bounded by 3/2. Next, we propose two
fully-distributed algorithms that provably converge to the unique NE without
revealing privacy-sensitive parameters from the slice tenants. Moreover, we
introduce an adaptive pricing mechanism of the wireless resources to improve
the network owner's profit. We evaluate the performance of our algorithms
through simulations and an experimental testbed deployed on the Amazon EC2
cloud, both based on a real-world dataset of base stations from the OpenCellID
project. Results conclude that our algorithms converge to the NE rapidly and
achieve near-optimal performance, while our pricing mechanism effectively
improves the profit of the network owner
Spatial Spectrum Access Game
A key feature of wireless communications is the spatial reuse. However, the
spatial aspect is not yet well understood for the purpose of designing
efficient spectrum sharing mechanisms. In this paper, we propose a framework of
spatial spectrum access games on directed interference graphs, which can model
quite general interference relationship with spatial reuse in wireless
networks. We show that a pure Nash equilibrium exists for the two classes of
games: (1) any spatial spectrum access games on directed acyclic graphs, and
(2) any games satisfying the congestion property on directed trees and directed
forests. Under mild technical conditions, the spatial spectrum access games
with random backoff and Aloha channel contention mechanisms on undirected
graphs also have a pure Nash equilibrium. We also quantify the price of anarchy
of the spatial spectrum access game. We then propose a distributed learning
algorithm, which only utilizes users' local observations to adaptively adjust
the spectrum access strategies. We show that the distributed learning algorithm
can converge to an approximate mixed-strategy Nash equilibrium for any spatial
spectrum access games. Numerical results demonstrate that the distributed
learning algorithm achieves up to superior performance improvement over a
random access algorithm.Comment: The paper has been accepted by IEEE Transactions on Mobile Computin