Combinatorial optimization: algorithms and complexity
Combinatorial optimization: algorithms and complexity
Probabilistic reasoning in intelligent systems: networks of plausible inference
Probabilistic reasoning in intelligent systems: networks of plausible inference
Neural Networks
Neural Computation
Intelligent Optimisation Techniques: Genetic Algorithms, Tabu Search, Simulated Annealing and Neural Networks
A Guide to the Literature on Learning Probabilistic Networks from Data
IEEE Transactions on Knowledge and Data Engineering
The computer for the 21st Century
IEEE Pervasive Computing
IEEE Wireless Communications
Service configuration and traffic distribution in composite radio environments
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
Enhancing Channel Estimation in Cognitive Radio Systems by means of Bayesian Networks
Wireless Personal Communications: An International Journal
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This paper discusses a management architecture for devices operating in heterogeneous environments, that enables access network selection through terminal-controlled, preference-based mechanisms. In this domain two problems are identified, mathematically formulated and solved: Intelligent Access Selection (IAS) and Modelling and Adaptation to User Preferences (MAUP). Their objective is to compute the optimal allocation of services to access networks and quality levels, and to dynamically determine user preferences according to the usage context, respectively. A greedy algorithm is proposed for the IAS problem, while the MAUP problem is handled through the construction of a Bayesian network that allows inference and learning of profile and usage patterns. Extensive simulation results of the proposed methods and algorithms are also presented.