1,515 research outputs found
Cognitive Hierarchy Theory for Distributed Resource Allocation in the Internet of Things
In this paper, the problem of distributed resource allocation is studied for
an Internet of Things (IoT) system, composed of a heterogeneous group of nodes
compromising both machine-type devices (MTDs) and human-type devices (HTDs).
The problem is formulated as a noncooperative game between the heterogeneous
IoT devices that seek to find the optimal time allocation so as to meet their
quality-of-service (QoS) requirements in terms of energy, rate and latency.
Since the strategy space of each device is dependent on the actions of the
other devices, the generalized Nash equilibrium (GNE) solution is first
characterized, and the conditions for uniqueness of the GNE are derived. Then,
to explicitly capture the heterogeneity of the devices, in terms of resource
constraints and QoS needs, a novel and more realistic game-theoretic approach,
based on the behavioral framework of cognitive hierarchy (CH) theory, is
proposed. This approach is then shown to enable the IoT devices to reach a CH
equilibrium (CHE) concept that takes into account the various levels of
rationality corresponding to the heterogeneous computational capabilities and
the information accessible for each one of the MTDs and HTDs. Simulation
results show that the proposed CHE solution keeps the percentage of devices
with satisfied QoS constraints above 96% for IoT networks containing up to
10,000 devices without considerably degrading the overall system performance.Comment: To appear in IEEE Transactions on Wireless Communications, 201
Review on Radio Resource Allocation Optimization in LTE/LTE-Advanced using Game Theory
Recently, there has been a growing trend toward ap-plying game theory (GT) to various engineering fields in order to solve optimization problems with different competing entities/con-tributors/players. Researches in the fourth generation (4G) wireless network field also exploited this advanced theory to overcome long term evolution (LTE) challenges such as resource allocation, which is one of the most important research topics. In fact, an efficient de-sign of resource allocation schemes is the key to higher performance. However, the standard does not specify the optimization approach to execute the radio resource management and therefore it was left open for studies. This paper presents a survey of the existing game theory based solution for 4G-LTE radio resource allocation problem and its optimization
Applications of Repeated Games in Wireless Networks: A Survey
A repeated game is an effective tool to model interactions and conflicts for
players aiming to achieve their objectives in a long-term basis. Contrary to
static noncooperative games that model an interaction among players in only one
period, in repeated games, interactions of players repeat for multiple periods;
and thus the players become aware of other players' past behaviors and their
future benefits, and will adapt their behavior accordingly. In wireless
networks, conflicts among wireless nodes can lead to selfish behaviors,
resulting in poor network performances and detrimental individual payoffs. In
this paper, we survey the applications of repeated games in different wireless
networks. The main goal is to demonstrate the use of repeated games to
encourage wireless nodes to cooperate, thereby improving network performances
and avoiding network disruption due to selfish behaviors. Furthermore, various
problems in wireless networks and variations of repeated game models together
with the corresponding solutions are discussed in this survey. Finally, we
outline some open issues and future research directions.Comment: 32 pages, 15 figures, 5 tables, 168 reference
Adaptive Power Allocation and Control in Time-Varying Multi-Carrier MIMO Networks
In this paper, we examine the fundamental trade-off between radiated power
and achieved throughput in wireless multi-carrier, multiple-input and
multiple-output (MIMO) systems that vary with time in an unpredictable fashion
(e.g. due to changes in the wireless medium or the users' QoS requirements).
Contrary to the static/stationary channel regime, there is no optimal power
allocation profile to target (either static or in the mean), so the system's
users must adapt to changes in the environment "on the fly", without being able
to predict the system's evolution ahead of time. In this dynamic context, we
formulate the users' power/throughput trade-off as an online optimization
problem and we provide a matrix exponential learning algorithm that leads to no
regret - i.e. the proposed transmit policy is asymptotically optimal in
hindsight, irrespective of how the system evolves over time. Furthermore, we
also examine the robustness of the proposed algorithm under imperfect channel
state information (CSI) and we show that it retains its regret minimization
properties under very mild conditions on the measurement noise statistics. As a
result, users are able to track the evolution of their individually optimum
transmit profiles remarkably well, even under rapidly changing network
conditions and high uncertainty. Our theoretical analysis is validated by
extensive numerical simulations corresponding to a realistic network deployment
and providing further insights in the practical implementation aspects of the
proposed algorithm.Comment: 25 pages, 4 figure
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