Design of the intelligent network segment of future mobile communication systems

Future mobile communcation systems will have to provide sophisticated services at quality levels comparable with those provided by fixed systems. An additional requirement is that these systems should be deployed by minimally impacting the existing infrastructures. In this respect an important component of future mobile communication systems is their intelligent network segment, which contains the logic of the services provided and the relevant data required for providing these services. In the context of this paper we study the problem of designing this segment. Our reference system is UMTS, but the practices presented herein may be applied to other systems as well. We identify two major problems, the first at the Mobility and Services Control Point (MSCP) level and the second at the Mobility and Services Data Point (MSDP) level. Our aim in this paper is the following. First, the overall design process is decomposed into a sequence of well-defined problems. Second, the problems are formally stated and theoretically formulated. Third, owing to the computational complexity associated with the theoretical formulations, we take the first steps towards the design of approximate (heuristic) algorithms. Finally we provide results and draw subsequent conclusions. © 1998 John Wiley & Sons, Ltd.     

A learning strategy for paging in mobile environments

The essence of designing a good paging strategy is to incorporate the user mobility characteristics in a predictive mechanism that reduces the average paging cost with as little computational effort as possible. In this scope, we introduce a novel paging scheme based on the concept of reinforcement learning. Learning endows the paging mechanism with the predictive power necessary to determine a mobile terminal's position, without having to extract a location probability distribution for each specific user. The proposed algorithm is compared against a heuristic randomized learning strategy akin to reinforcement learning, that we invented for this purpose, and performs better than the case where no learning is used at all. It is shown that if the user normally moves only among a fraction of cells in the location area, significant savings can be achieved over the randomized strategy, without excessive time to train the network. Copyright © 2003 John Wiley & Sons, Ltd.     

Enhanced estimation of configuration capabilities in cognitive radio [cognitive radio communications and networks]

Cognitive radio is a highly promising answer to the complexity and heterogeneity characterizing the beyond 3G wireless scenario. In this context, this article advances from the field of interference sensing to the fields of (basic) reasoning and robust reasoning. Interference sensing is concerned with the acquisition of interference related measurements for frequency bands of interest. The article describes how a cognitive radio system can reason on these measurements to obtain estimations for the capabilities of alternate configurations, especially in terms of achievable transmission capacity and coverage. Subsequently, it focuses on robust reasoning, namely, on enhancing these estimations by employing machine learning, which constitutes an important aspect of cognitive radio. Several relevant solutions are sketched and explained, with a view to providing a complete picture.     

QoS Management by Means of Application Control

The complexity and sophistication of integratedbroadband networks call for a broad repertoire ofmanagement methods. Foreseen intensive competition ofthe communications market, and the upgrade ofend-customer's satisfaction as the focal point of anymanagement policy, raise a very important problem: howto utilize efficiently the resources of the networkwhile maintaining the Quality of Service (QoS) provided to end-users within a pre-negotiated range. Inthis paper, a QoS management architecture for multimediaapplications based on an application control scheme isproposed. Adhering to the Total Quality Management (TQM) paradigm, we let management span over theentire communication stack, the entire network(end-to-end) and the entire call duration. Applicationcontrol scheme aims to meet pre-specified QoS goals by adjusting the traffic produced by theend-user applications. The adjustment technique is basedon the notion that each multimedia session is allowed tooperate on a set of discrete application quality levels. A method for monitoring and assessingthe end-user perceived quality is presented. Themathematical problem of optimal resources allocation andan associated heuristic are specified. Finally, a specific instantiation of the introduced QoSmanagement architecture on a real testbed is describedand experimentation results are presented andevaluated.     

Traffic-adaptive aggregate channel allocation for future cellular communication systems

A fundamental problem in third-generation mobile systems is the adaptation of the channel allocation to the traffic volume variation. Traffic adaptation may be partly achieved by the following (aggregate channel allocation) problem: given the set of channels, the cell structure and the load to be accommodated in each cell within a certain time zone, find the optimal allocation of channels to cells subject to the restrictions arising from the minimum permissible distance of cells where the same channel can be used at the same time. In this paper the problem is optimally formulated and efficiently solved by a heuristic. In the sequel the performance of a combined channel management scheme that uses the above problem for handling major traffic variations and a distributed DCA scheme for handling local traffic variations is assessed. Finally, results are presented and some concluding remarks are made. © 1998 John Wiley & Sons, Ltd.     

Cost-Efficient Design of Future Broadband Fixed Wireless Access Systems

In future broadband fixed wireless access systems the overall design procedure is critical for their successful commercial deployment as well as their efficient operation and management. The problem addressed in this paper is twofold. Specifically, at a first phase the radio access network planning problem is addressed, which aims at finding the minimum-cost configuration of Access Point Transceivers (APTs) given thegeographical layout of the area to be covered. At the second phase, the interconnecting planning problem is addressed and aims at finding the minimum-cost configuration of the AccessPoint Controllers (APCs) and Inter-Working Units (IWUs) given the Access PointTransceivers layout. Both problems are formally defined, optimally formulated, and solved by computationally efficient heuristics. Finally, results are provided and subsequent conclusions are drawn.     

Management of the power control operation in HIPERLAN/2 networks

BRAN/WLAN systems, e.g., HIPERLAN/2, IEEE 802.11a, etc., are seen as a promising solution for covering residential, business, transport, tourism, etc., environments, and generally areas of high demand, characterized as “hot spots”. This paper presents management functionality for augmenting the potential exploitation of one of these systems, HIPERLAN/2, by making feasible their (loose or tight) integration in a composite radio (CR) context. The approach will be the following. The first point will be to revisit the main features of a HIPERLAN/2 system. Next, the architecture of a general Service and Network Management System (SNMS), which has been developed for assisting wireless systems in their operation in a CR context, will be briefly presented. The next main point will be the presentation of the functionality of the SNMS component that is tailored to the managed HIPERLAN/2 technology and specifically to the configuration of the Power Control (PC) functionality. An algorithm for configuring the PC operation, based on a greedy algorithm and a neural network, will be presented. A relevant resource management problem, which should be efficiently solved for exploiting HIPERLAN/2 networks, will be addressed. Numerical results will be presented. Acknowledgment This work was partially funded by the Commission of the European Communities, under the Fifth Framework Program, within the IST project MONASIDRE (IST-2000-26144: Management of Networks and Services in Diversified Radio Environment).     

Network Load Predictions Based on Big Data and the Utilization of Self-Organizing Maps

The pervasiveness of computers in everyday life has already increased and keeps increasing the available digital data both in volume and variety/disparity. This large and dynamic availability of digital data is referred to as Big Data and is very promising in bringing forward new insights and knowledge. For obtaining these insights, the proper combination and processing of the data is required. However, the dynamicity and the increasing size of data start making their handling impossible for analysts and raise many concerns on the manner in which data will be processed from now on. Towards this direction, this paper proposes a tool that processes and combines disparate data in order to create insights regarding a future network load. In particular, the tool (based on the unsupervised machine learning technique of Self-Organizing Maps) builds knowledge on the network load that is encountered with respect to the date of interest, the location, the weather, and the features of the day (e.g., weekend, bank holiday, etc.). The obtained results reveal that the tool is capable of learning the traffic pattern and thus predicting the network load that will be encountered in the near or distant future given information for the above presented parameters with small deviations (up to 0.000553 in terms of Mean Square Error). Moreover, the tool maintains only the most representative data instances and thus reduces the data storage requirements with no loss of information.     

Enhancing Channel Estimation in Cognitive Radio Systems by means of Bayesian Networks

This paper proposes enhancements to the channel(-state) estimation phase of a cognitive radio system. Cognitive radio devices have the ability to dynamically select their operating configurations, based on environment aspects, goals, profiles, preferences etc. The proposed method aims at evaluating the various candidate configurations that a cognitive transmitter may operate in, by associating a capability e.g., achievable bit-rate, with each of these configurations. It takes into account calculations of channel capacity provided by channel-state estimation information (CSI) and the sensed environment, and at the same time increases the certainty about the configuration evaluations by considering past experience and knowledge through the use of Bayesian networks. Results from comprehensive scenarios show the impact of our method on the behaviour of cognitive radio systems, whereas potential application and future work are identified.