An overview of learning mechanisms for cognitive systems
- PDF / 193,430 Bytes
- 6 Pages / 595.28 x 793.7 pts Page_size
- 71 Downloads / 201 Views
REVIEW
Open Access
An overview of learning mechanisms for cognitive systems Aimilia Bantouna1*, Vera Stavroulaki1, Yiouli Kritikou1, Kostas Tsagkaris1, Panagiotis Demestichas1 and Klaus Moessner2
Abstract Cognitive systems were first introduced by Mitola and in the last decade they have proved to be beneficial in selfmanagement functionalities of future generation networks. The advantages and the way that networks gain benefits from cognitive systems is analysed in this article. Moreover, since such systems are closely related to machine learning, the focus of this article is also placed on machine learning techniques applied both in the network and the user devices side. In particular, celebrating 10 years of cognitive systems, this survey-oriented article presents an extended state-of-the-art of machine learning applied to cognitive systems as coming from the recent research and an overview of three different learning capabilities of both the network and the user device. Keywords: learning, neural networks, Bayesian networks, self-organizing maps (SOMs)
1. Introduction The success of mobile networks has been driven by the services offered, i.e. voice in second generation and multimedia services in third generation (3G) networks. Similarly, a key issue for the success of future generation networks is considered to be the provision of enhanced, always available, personalised services. In addition to communication and entertainment, a wide range of other life sectors can benefit from evolving multimedia applications, including healthcare, environmental monitoring, transportation and public safety. In this respect, it is necessary to develop mechanisms that will enhance the end-user experience, in terms of quality of service (QoS), availability and reliability. At the same time, the complexity and heterogeneity of the infrastructure of mobile network operators increases as radio access technologies (RATs) continue to evolve and new ones emerge. In summary, fundamental requirements for the success of future networks are service personalisation, always-best-connectivity, ubiquitous service provision as well as efficient handling of the complexity of the underlying infrastructure. All these call for self-management and learning capabilities in future generation * Correspondence: [email protected] 1 Department of Digital Systems, University of Piraeus, 80, Karaoli & Dimitriou Str., Piraeus, Greece Full list of author information is available at the end of the article
network systems. Self-management enables a system to identify opportunities for improving its performance and configuring/adapting its operation accordingly without the need for human intervention [1] Learning mechanisms are essential so as to increase the reliability of decision making. Learning mechanisms also provide the ground for enabling proactive handling of problematic situations, i.e. identifying and handling issues that could undermine the performance of the system before these actually occur. In this respect, cognitive, reconfigurable systems
Data Loading...
