9,702 research outputs found
Energy-Aware Competitive Power Allocation for Heterogeneous Networks Under QoS Constraints
This work proposes a distributed power allocation scheme for maximizing
energy efficiency in the uplink of orthogonal frequency-division multiple
access (OFDMA)-based heterogeneous networks (HetNets). The user equipment (UEs)
in the network are modeled as rational agents that engage in a non-cooperative
game where each UE allocates its available transmit power over the set of
assigned subcarriers so as to maximize its individual utility (defined as the
user's throughput per Watt of transmit power) subject to minimum-rate
constraints. In this framework, the relevant solution concept is that of Debreu
equilibrium, a generalization of Nash equilibrium which accounts for the case
where an agent's set of possible actions depends on the actions of its
opponents. Since the problem at hand might not be feasible, Debreu equilibria
do not always exist. However, using techniques from fractional programming, we
provide a characterization of equilibrial power allocation profiles when they
do exist. In particular, Debreu equilibria are found to be the fixed points of
a water-filling best response operator whose water level is a function of
minimum rate constraints and circuit power. Moreover, we also describe a set of
sufficient conditions for the existence and uniqueness of Debreu equilibria
exploiting the contraction properties of the best response operator. This
analysis provides the necessary tools to derive a power allocation scheme that
steers the network to equilibrium in an iterative and distributed manner
without the need for any centralized processing. Numerical simulations are then
used to validate the analysis and assess the performance of the proposed
algorithm as a function of the system parameters.Comment: 37 pages, 12 figures, to appear IEEE Trans. Wireless Commu
Joint Channel Selection and Power Control in Infrastructureless Wireless Networks: A Multi-Player Multi-Armed Bandit Framework
This paper deals with the problem of efficient resource allocation in dynamic
infrastructureless wireless networks. Assuming a reactive interference-limited
scenario, each transmitter is allowed to select one frequency channel (from a
common pool) together with a power level at each transmission trial; hence, for
all transmitters, not only the fading gain, but also the number of interfering
transmissions and their transmit powers are varying over time. Due to the
absence of a central controller and time-varying network characteristics, it is
highly inefficient for transmitters to acquire global channel and network
knowledge. Therefore a reasonable assumption is that transmitters have no
knowledge of fading gains, interference, and network topology. Each
transmitting node selfishly aims at maximizing its average reward (or
minimizing its average cost), which is a function of the action of that
specific transmitter as well as those of all other transmitters. This scenario
is modeled as a multi-player multi-armed adversarial bandit game, in which
multiple players receive an a priori unknown reward with an arbitrarily
time-varying distribution by sequentially pulling an arm, selected from a known
and finite set of arms. Since players do not know the arm with the highest
average reward in advance, they attempt to minimize their so-called regret,
determined by the set of players' actions, while attempting to achieve
equilibrium in some sense. To this end, we design in this paper two joint power
level and channel selection strategies. We prove that the gap between the
average reward achieved by our approaches and that based on the best fixed
strategy converges to zero asymptotically. Moreover, the empirical joint
frequencies of the game converge to the set of correlated equilibria. We
further characterize this set for two special cases of our designed game
Game Theoretic Approaches to Massive Data Processing in Wireless Networks
Wireless communication networks are becoming highly virtualized with
two-layer hierarchies, in which controllers at the upper layer with tasks to
achieve can ask a large number of agents at the lower layer to help realize
computation, storage, and transmission functions. Through offloading data
processing to the agents, the controllers can accomplish otherwise prohibitive
big data processing. Incentive mechanisms are needed for the agents to perform
the controllers' tasks in order to satisfy the corresponding objectives of
controllers and agents. In this article, a hierarchical game framework with
fast convergence and scalability is proposed to meet the demand for real-time
processing for such situations. Possible future research directions in this
emerging area are also discussed
A Comprehensive Survey of Potential Game Approaches to Wireless Networks
Potential games form a class of non-cooperative games where unilateral
improvement dynamics are guaranteed to converge in many practical cases. The
potential game approach has been applied to a wide range of wireless network
problems, particularly to a variety of channel assignment problems. In this
paper, the properties of potential games are introduced, and games in wireless
networks that have been proven to be potential games are comprehensively
discussed.Comment: 44 pages, 6 figures, to appear in IEICE Transactions on
Communications, vol. E98-B, no. 9, Sept. 201
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