Читать книгу The Smart Cyber Ecosystem for Sustainable Development - Группа авторов - Страница 38

Nowadays, communication networks are getting more complex and their configuration and management to achieve performance goals have become a challenging task. This is due to the following:

 The significant increase in the number of network users.

 The increase of the number of required networking elements at the network core.

 The huge number of mobile applications.

 The diversity of traffic.

The idea of cognitive networks is to improve the performance of networks and reduce the effort required for their configuration and management. Unlike current technologies, in which networking elements are unable to make intelligent decisions, the elements of a cognitive network have the ability to learn and dynamically self-adjust as response to changing channel and network conditions. Cognitive network elements utilize the principles of logic and learning in order to improve performance. Decisions are made to improve the overall network performance, rather than the performance of individual network elements. Thus, cognitive networks achieve the goal of intelligent, self-adjustment, and improved network performance, by intelligently finding optimal values of many adjustable parameters. They are required to learn the relationships among network parameters of the entire protocol stack.

As we indicated, a cognitive network should provide better performance to users. The cognition can be used to improve: utilization of network resources, QoS, security, access, control, or any other issue related to network management.

It must be emphasized that cognition is not only related to wireless networks, but also the idea applies to the management of network infrastructure and the various network elements [3]. To stimulate transition to cognitive networks, their performance must outweigh all additional complexities that they require. The question is how to measure the cost of a cognitive network. Such cost would primarily depend on the communications required to apply cognition, the architecture complexity, maintenance cost, and the operational complexity. For example, in wired networks, user’s behavior is clear and easily predictable, and therefore, it may not be interesting for some people to employ cognition with this type of networks. On the contrary, wireless networks often include heterogeneous elements and have characteristics that cannot be easily predicted, making them the best candidates to adopt the cognition concept.

Cognitive networks should use different measures, tools, and patterns as inputs to the decision-making processes. Then, they come up with results in the form of procedures or commands that can be implemented in modifiable network elements. It is important to note that the cognitive network must adapt to changes in the environment in which it operates and anticipate problems before they occur. Their architecture must be flexible, scalable and be supportive of future improvements and extensions.

Several research studies have been discussing the architecture and functionalities of cognitive networks. There is a need to rethink about network intelligence from being dependent on resource management to understanding the needs of network users and then transferring intelligence also to the elements of the network.

The central mechanism of the cognitive network is the cognitive process. This process implements real learning and decides the appropriate responses and actions based on observations in the network. The operation of the cognitive process mainly depends on whether its implementation is central or distributive as well as on the amount of state network information.