Enhancing cognitive radio systems with robust reasoning

Cognitive radio systems dynamically reconfigure the algorithms and parameters they use, in order to adapt to the changing environment conditions. However, reaching proper reconfiguration decisions presupposes a way of knowing, with high enough assurance, the capabilities of the alternate configurations, especially in terms of achievable transmission capacity and coverage. The present paper addresses this problem, firstly, by specifying a complete process for extracting estimations of the capabilities of candidate configurations, in terms of transmission capacity and coverage, and, secondly, by enhancing these estimations with the employment of a machine learning technique. The technique is based on the use of Bayesian Networks, in conjunction with an effective learning and adaptation strategy, and aims at extracting and exploiting knowledge and experience, in order to reach robust (i.e. stable and reliable) estimations of the configurations' capabilities. Comprehensive results of the proposed method are presented, in order to validate its functionality. Copyright © 2007 John Wiley & Sons, Ltd.     

Information Flow for Optimized Management of Spectrum and Radio Resources in Cognitive B3G Wireless Networks

The Beyond 3G (B3G) radio landscape will consist of cognitive heterogeneous wireless networks, operating in the framework of diverse co-operative associations among different classes of operators and providers, for the accommodation of the demands of users with multimode and/or multihoming enabled terminals. In this context, the optimized spectrum and radio resource utilization will be key factor for accomplishment of the purposes of both users and operators/providers, namely the satisfaction of user’s needs and the augmentation of profit, respectively. In this paper, we focus on an architecture for the management and optimization of spectrum and radio resource utilization in such composite wireless environments, and we analytically present the respective information flow among and from/to the functional entities involved in this architecture. The proposed management architecture can operate in the framework of different business scenarios and is based on related work that has been conducted within the IEEE 1900.4 standard.     

Enhanced network selections in a cognitive wireless B3G world

A network operator of the wireless beyond the third generation (B3G) era will need to be directing terminals to the most appropriate radio networks of its heterogeneous infrastructure. This requires advanced terminal management functionality for conducting optimal network selections, in a seamless to the user manner. This paper enhances network selection schemes by developing functionality for learning the quality of service (QoS) capabilities of candidate networks and by exploiting the acquired knowledge in the network selection. The resulting scheme selects networks that offer the best QoS, taking into account the user needs, policies, the context of operation, and the knowledge. The advantage is that the reliability of the knowledge-based decisions is increased. This enhances the probability of offering the desired QoS to the user and reduces some of the related operational overheads in the network. The learning functionality is influenced by Bayesian networks. The computational effort for the development of the knowledge is shown to be minimal. Results show the behavior of the scheme in terms of knowledge development, respective computational effort required, and network selections made. In general, the paper contributes to the introduction of cognitive systems in the wireless B3G world.     

Service configuration and user profiling in 4G terminals

This paper presents a middleware platform for managing devices that operate in heterogeneous environments. The proposed management framework supports terminal-controlled, preference-based access network selection. Two separate problems are identified in this domain: one involving the computation of optimal allocations of services to access networks and quality levels (service configuration), and one concerning the dynamic inference of the user’s preferences, according to the usage context (user profiling). This paper includes an approach to the definition, mathematical formulation and solution of both these problems. Indicative results of the proposed solution methods are presented in the context of a real-life scenario simulating a day in the life of an ordinary user.     

Intelligent discovery of the capabilities of reconfiguration options in a cognitive wireless B3G context

Beyond 3G (B3G) wireless connectivity can efficiently be realized by exploiting cognitive networking concepts. Cognitive systems dynamically reconfigure the radio access technologies and the spectrum they use, based on experience, in order to adapt to the changing environment conditions. However, dynamic reconfiguration decisions call for robust discovery, i.e., radio-scene analysis and channel identification schemes. This paper intends to contribute in the areas of radio-scene analysis and channel identification: first, by providing an overview of interference estimation methods, and explaining how capacity estimations can be derived based on the measured interference levels; second, by specifying the information flow for the radio-scene analysis process of a cognitive radio system; and third, by enhancing the above with a learning system, which is essential for obtaining a truly cognitive process. The proposed approach lies in the introduction of a robust probabilistic model for optimal prediction of the capabilities of alternative configurations, in terms of capacity.     

Towards Anonymous Mobile Community services

In the current market conditions, network operators are in search of novel value-added services that will increase their revenue. This paper introduces the innovative concept of Anonymous Mobile Community (AMC) services and thoroughly defines and describes a robust platform targeted for their deployment. AMC services take advantage of the terminals’ capabilities to collect information and deliver it to the network. In this context, terminals are enabled to form communities that serve as sources of information. In these communities, the anonymity and privacy of the end-users are respected and guarded. Several examples of promising AMC services are presented and categorized. An indicative example application is the provision of real-time information regarding road-traffic conditions, based on the location and speed of mobile terminals. A system aiming at the provision of diverse AMC services is proposed, and its requirements, architecture and functionality are described in detail. The related scalability issues and business models are carefully outlined, and a use case scenario as well as trial results are presented.     

The E3 architecture: enabling future cellular networks with cognitive and self‐x capabilities

Future mobile networks are expected to be complex heterogeneous systems. On the one hand this will enable users to take advantage of a number of different access technologies. On the other hand it will seriously affect network management procedures since more extensive operations and decisions will have to be dealt with. To tackle these challenges a number of new dynamic mechanisms need to be designed. It is imperative that certain network management tasks have to be performed without human intervention to reduce the OPEX costs and achieve faster responses in different events. To achieve this goal, the introduction of self‐x functionalities, combined with cognitive mechanisms and the ability to reconfigure network entities and terminals, is required. Moreover, the introduction of a new pilot channel needs to be considered to assist the terminals in selecting the most suitable radio access technology according to their requirements. We present the functional architecture of an evolved network that was designed in the context of the EU‐funded IP project ‘E³: End‐to‐End Efficiency’. This architecture aims to enhance existing procedures usually performed in traditional operation and maintenance systems (e.g. spectrum management, network planning, configuration actions). We explain the rationale of our design and provide specific examples to illustrate the role of the different functional entities and their interfaces. A considerable part of this architecture has recently been approved as a feasibility study by the ETSI Committee Reconfigurable Radio System. Copyright © 2010 John Wiley & Sons, Ltd.     

Policies for the reconfiguration of cognitive wireless infrastructures to 3G Radio Access Technologies

The ceaseless evolution of wireless communications is reflected nowadays on the introduction of Beyond-3G (B3G) systems, characterized by the coexistence and cooperation of various Radio Access Technologies (RATs), over a common infrastructure. Major facilitator of this convergence is the advent of cognitive networks, which deploy elements (base stations and mobile terminals) that are able to proactively adapt to environmental stimuli, so that to optimize their performance. Part of the adaptation action takes place in cognitive base stations that own several reconfigurable transceivers, which are controlled by appropriate management functionality and may dynamically change their operating parameters. Each reconfiguration set includes a specific RAT, carrier frequency, as well as demand volume to be allocated per transceiver. Accordingly, proper evaluation of the various candidate reconfiguration sets appears to be of high significance. To this effect, in this paper we consider a cognitive network segment with transceivers operating at 3G RAT/carrier and we solve the DAMC problem (Demand Allocation into Multiple Carriers problem), which aims at evaluating and selecting the optimum policy to allocate the demand into the available 3G carrier frequencies. Optimality is expressed in terms of minimizing the total transmitted/received power per base station, thus deciding for the reconfigurations with the least impact on network interference. Indicative simulation scenarios and results are also presented for the validation and verification of the proposed functionality.