Resource allocation algorithms in wireless networks can require solving complex optimization problems at every decision epoch. For large scale networks, when decisions need to be taken on time scales of milliseconds, using standard convex optimization solvers for computing the optimum can be a time-consuming affair that may impair real-time decision making. In this paper, we propose to use Data-driven and Deep Feedforward Neural Networks (DFNN) for learning the relation between the inputs and the outputs of two such resource allocation algorithms that were proposed in Nguyen et al. (2019, 2020). On numerical examples with realistic mobility patterns, we show that the learning algorithm yields an approximate yet satisfactory solution with much less computation time.
With the advent of big data and the emergence of data markets, preserving individuals’ privacy has become of utmost importance. The classical response to this need is anonymization, i.e., sanitizing the information that, directly or indirectly, can allow users’ re-identification. Among the various approaches, -anonymity provides a simple and easy-to-understand protection. However, -anonymity is challenging to achieve in a continuous stream of data and scales poorly when the number of attributes becomes high.
In this paper, we study a novel anonymization property called -anonymity that we explicitly design to deal with data streams, i.e., where the decision to publish a given attribute (atomic information) is made in real time. The idea at the base of -anonymity is to release such attribute about a user only if at least other users have exposed the same attribute in a past time window. Depending on the value of , the output stream results -anonymized with a certain probability. To this end, we present a probabilistic model to map the -anonymity into the -anonymity property. The model is not only helpful in studying the -anonymity property, but also general enough to evaluate the probability of achieving -anonymity in data streams, resulting in a generic contribution.
We establish stability criterion for a two-class retrial system with Poisson inputs, general class-dependent service times and class-dependent constant retrial rates. We also characterize an interesting phenomenon of partial stability when one orbit is tight but the other orbit goes to infinity in probability. All theoretical results are illustrated by numerical experiments.