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网络领域最新进展--感知网络国外经典书集

2011-12-07 上传大小:2.98MB
For complex computer networks with many tunable parameters and network performance
objectives, the task of selecting the ideal network operating state is difficult. To improve
the performance of these kinds of networks, this research proposes the idea of the cognitive
network. A cognitive network is a network composed of elements that, through learning and
reasoning, dynamically adapt to varying network conditions in order to optimize end-to-end
performance. In a cognitive network, decisions are made to meet the requirements of th
                            e
network as a whole, rather than the individual network components.
We examine the cognitive network concept by first providing a definition and then outlin-
ing the difference between it and other cognitive and cross-layer technologies. From this
definition, we develop a general, three-layer cognitive network framework, based loosely on
the framework used for cognitive radio. In this framework, we consider the possibility of a
cognitive process consisting of one or more cognitive elements, software agents that operate
somewhere between autonomy and cooperation.
To understand how to design a cognitive network within this framework we identify three
critical design decisions that affect the performance of the cognitive network: the selfishness
of the cognitive elements, their degree of ignorance, and the amount of control they have over
the network. To evaluate the impact of these decisions, we created a metric called the price
of a feature, defined as the ratio of the network performance with a certain design decision
to the performance without the feature.
To further aid in the design of cognitive networks, we identify classes of cognitive networks
that are structurally similar to one another. We examined two of these classes: the po-
tential class and the quasi-concave class. Both classes of networks will converge to Nash
Equilibrium under selfish behavior and in the quasi-concave class this equilibrium is both
Pareto and globally optimal. Furthermore, we found the quasi-concave class has other desir-
able properties, reacting well to the absence of certain kinds of information and degrading
gracefully under reduced network control.
In addition to these analytical, high level contributions, we develop cognitive networks for
two open problems in resource management for self-organizing networks, validating and
illustrating the cognitive network approach. For the first problem, a cognitive network is
shown to increase the lifetime of a wireless multicast route by up to 125%. For this problem,
we show that the price of selfishness and control are more significant than the price of
ignorance. For the second problem, a cognitive network minimizes the transmission power
and spectral impact of a wireless network topology under static and dynamic conditions. The
cognitive network, utilizing a distributed, selfish approach, minimizes the maximum power in the topology and reduces (on average) the channel usage to within 12% of the minimum
channel assignment. For this problem, we investigate the price of ignorance under dynamic
networks and the cost of maintaining knowledge in the network.
Today’s computer networking technology will not be able to solve the complex problems
that arise from increasingly bandwidth-intensive applications competing for scarce resources.
Cognitive networks have the potential to change this trend by adding intelligence to the
network. This work introduces the concept and provides a foundation for future investigation
and implementation.                        
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