Allocation of telecommunication systems bandwidth is provided preferably in a predictive fashion. Packets are identified with particular data streams and characteristics of the data streams are used to predict probable future bandwidth requirements. Such predictions are used to allocate high-bandwidth channels, such as ISDN B channels and to close or switch channels as in accordance with predicted needs. Preferably the system is self-learning and can modify a rules base for making allocation decisions e.g. based on actual use statistics.

Predictive bandwidth allocation method and apparatus

Artificial intelligence (AI) has been applied to the telecommunications industry for more than a decade. The purpose of this paper is to examine the application of AI in the telecommunications industry sector. Our research finds that AI's first main application in telecommunications is in the network management area. Expert systems and machine learning are the two AI techniques that have been widely used in telecommunications, while machine learning and distributed artificial intelligence are the two AI techniques which are most promising for the future. The research also finds that different AI techniques have their unique applications in the telecommunications industry.

Artificial intelligence applications in the telecommunications industry

Cooperative management is concerned with the management of networks and services involving the cooperation of a number of human/organizational entities. One of the prerequisites for efficient management of these complex systems is related to understanding of the roles of humans and the ways hey interact with each other. Cooperative management Methodology for Enterprise Networks (CoMEN) achieves these objectives by defining an abstract measure of cooperation called Awareness level that is based on Computer Supported Cooperative Work (CSCW) concepts and techniques. In view of the abstract nature of the awareness level definitions, it is not clear how abstract awareness levels can be accurately translated into equivalent cooperative management design parameters. This paper explores the notion of fuzzy sets that enables the use of linguistic values for awareness levels. This is aimed at unveiling of the deficiencies in the existing collaborative support tools with a view to developing more effective cooperative applications. We also model the CSCW tools in terms of repositories and communication mechanisms using fuzzy notions with a view to arrive at a formal design methodology for cooperative management systems. The idea has been illustrated with a case study in a large telecom organization.

The Role of Fuzzy Awareness Modelling in Cooperative Management

The ever evolving telecommunication networks in terms of their technology, infrastructure, and supported services have always posed challenges to the network managers to come up with an efficient Network Management System (NMS) for effective network management. The need for automated and efficient management of the current networks, more specifically the Next Generation Network (NGN), is the subject addressed in this research. A detailed description of the management challenges in the context of current networks is presented and then this work enlists the desired features and characteristics of an efficient NMS. It then proposes that there is a need to apply Artificial Intelligence (AI) and Machine Learning (ML) approaches for enhancing and automating the functions of NMS. The first contribution of this work is a comprehensive survey of the AI and ML approaches applied to the domain of NM. The second contribution of this work is that it presents the reasoning and evidence to support the choice of Bayesian Networks (BN) as a viable solution for ML-based NMS. The final contribution of this work is that it proposes and implements three novel NM solutions based on the BN approach, namely BN-based Admission Control (BNAC), BN-based Distributed Admission Control (BNDAC) and BN-based Intelligent Traffic Engineering (BNITE), along with the description of algorithms underpinning the proposed framework.

Application of Bayesian Networks for Autonomic Network Management

SUCCESS FACTORS IMPACTING ARTIFICIAL INTELLIGENCE ADOPTION--PERSPECTIVE FROM THE TELECOM INDUSTRY IN CHINA Hong Chen Old Dominion University, 2019 Director: Dr. Ling Li As the core driving force of the new round of informatization development and the industrial revolution, the disruptive achievements of artificial intelligence (AI) are rapidly and comprehensively infiltrating into various fields of human activities. Although technologies and applications of AI have been widely studied, and factors that affect AI adoption are identified in existing literature, the impact of success factors on AI adoption remains unknown. Accordingly, the main study of this paper proposes a framework to explore the effects of success factors on AI adoption by integrating the technology, organization, and environment (TOE) framework and diffusion of innovation (DOI) theory. Particularly, this framework consists of factors regarding the external environment, organizational capabilities, and innovation attributes of AI. The framework is empirically tested with data collected by surveying telecom companies in China. Structural equation modeling is applied to analyze the data. The results indicate that compatibility, relative advantage, complexity, managerial support, government involvement, and vendor partnership are significantly related to AI adoption. Managerial capability impacts other organizational capabilities and innovation attributes of AI, but it is indirectly related to AI adoption. Market uncertainty and competitive pressure are not significantly related to AI adoption, but all the external environment factors positively influence managerial capability. The study provides support for firms' decision-making and resource allocation regarding AI adoption. In addition, based on the resource-based view (RBV), this article conducts study 2 which explores the factors that influence the firm sustainable growth. Multiple regression model is applied to empirically test the hypotheses with longitudinal time-series panel data from telecom companies in China. The results indicate that at the firm level, the customer value and operational expenses are significantly related to sustainable growth. Also, at the industry level, industry investment significant impacts sustainable growth. Study 2 provides insights for practitioners the way to keep sustainable growth.

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Success Factors Impacting Artificial Intelligence Adoption --- Perspective From the Telecom Industry in China

As society is propelled towards the information age, the key component of this change, the telecommunications network faces rapid and profound transformations. Novel approaches such as artificial intelligence (AI) will be essential to achieve these transformations efficiently. The paper is aimed at those engineers who, although familiar with telecommunications, may be unsure of artificial intelligence techniques. The document highlights the necessity for AI in telecommunications and provides a survey of AI techniques. Finally, it describes work being carried out in this field at Strathclyde University. >

Artificial intelligence in telecommunications

The next generation of telecommunications networks-the giant gigabit networks-will require the present management systems to undergo some radical changes. In these networks, the link speeds will move up to gigabits per second and traffic control will become extremely difficult. Moreover, these networks will contain a very large number of physical and logical elements, all these elements will need to be managed and this may rapidly lead to an information explosion. As a consequence, many traditional control and management techniques will no longer be appropriate. Other approaches such as artificial intelligence (AI) have to be considered. This paper concentrates on this issue and highlights the necessity for the use of AI in the management of broadband networks.

Emerging AI techniques for network management

The authors highlight the reasons why AI is an appropriate solution to telecommunications problems and present several AI techniques and their applications. They also highlight the area of resource management and present the work being carried out at Strathclyde University, in this field. The system developed for resource allocation problems has three driving forces: a stochastic nature, a distributed architecture and some local improvement facilities, and offers some promising results.

http://ieeexplore.ieee.org/iel3/1184/5872/00224625.pdf

D.G. Smith

Papers

Artificial intelligence in telecommunications

Emerging AI techniques for network management

Artificial intelligence for resource management in telecommunications