91,066 research outputs found
Performance of MIMO Cognitive Ad-hoc Networks
Cognitive ad-hoc networks are able to share primary user frequency bands following certain interference preconditions. For considered cognitive network, cognitive communication is limited by the interference
imposed on the primary user. Probability of channel availability for cognitive nodes for such opportunistic access is determined. Furthermore, this probability of channel availability is used for the performance analysis purpose. A Carrier Sense Multiple Access (CSMA) Media Access Control (MAC) protocol for the cognitive network is considered and for that the embedded Markov model of cognitive nodes is determined. This Markov model is used to determine the average channel access delay, throughput and service rate of cognitive nodes.
This network is further extended to consider multiple frequency bands for cognitive access. For this propose algorithms are proposed to address the channel allocation and fairness issues of multi-band multiuser cognitive ad-hoc networks. Nodes in the network have unequal channel access probability and have no prior information about the offered bandwidth or number of users in the multiple access system. In that, nodes use reinforcement learning
algorithm to predict future channel selection probability from the past experience and reach an equilibrium state. Proof of convergence of this multi party stochastic game is established. Nevertheless, cognitive nodes can reduce the convergence time by exchanging channel selection information and thus further improve the network performance.
To further improve the spectrum utilization, this study is extended to include Multiple-input Multiple-output (MIMO)
techniques. To improve the transmission efficiency of the MIMO system, a cross-layer antenna selection algorithm is proposed. The proposed cross-layer antenna selection and beamforming algorithm works as the data link layer efficiency information is used for antenna selection
purpose to achieve high efficiency at the data link layer.
Having analyzed the cognitive network, to consider more realistic scenario primary users identification method is proposed. An artificial intelligent method has been adopted for this purpose. Numerical results are presented for the algorithm and compare these results with the theoretical ones
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
Computational Intelligence Inspired Data Delivery for Vehicle-to-Roadside Communications
We propose a vehicle-to-roadside communication protocol based on distributed clustering where a coalitional game approach is used to stimulate the vehicles to join a cluster, and a fuzzy logic algorithm is employed to generate stable clusters by considering multiple metrics of vehicle velocity, moving pattern, and signal qualities between vehicles. A reinforcement learning algorithm with game theory based reward allocation is employed to guide each vehicle to select the route that can maximize the whole network performance. The protocol is integrated with a multi-hop data delivery virtualization scheme that works on the top of the transport layer and provides high performance for multi-hop end-to-end data transmissions. We conduct realistic computer simulations to show the performance advantage of the protocol over other approaches
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
Energy-Efficient Resource Allocation in Wireless Networks: An Overview of Game-Theoretic Approaches
An overview of game-theoretic approaches to energy-efficient resource
allocation in wireless networks is presented. Focusing on multiple-access
networks, it is demonstrated that game theory can be used as an effective tool
to study resource allocation in wireless networks with quality-of-service (QoS)
constraints. A family of non-cooperative (distributed) games is presented in
which each user seeks to choose a strategy that maximizes its own utility while
satisfying its QoS requirements. The utility function considered here measures
the number of reliable bits that are transmitted per joule of energy consumed
and, hence, is particulary suitable for energy-constrained networks. The
actions available to each user in trying to maximize its own utility are at
least the choice of the transmit power and, depending on the situation, the
user may also be able to choose its transmission rate, modulation, packet size,
multiuser receiver, multi-antenna processing algorithm, or carrier allocation
strategy. The best-response strategy and Nash equilibrium for each game is
presented. Using this game-theoretic framework, the effects of power control,
rate control, modulation, temporal and spatial signal processing, carrier
allocation strategy and delay QoS constraints on energy efficiency and network
capacity are quantified.Comment: To appear in the IEEE Signal Processing Magazine: Special Issue on
Resource-Constrained Signal Processing, Communications and Networking, May
200
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