A learning approach for prioritized handoff channel allocation in mobile multimedia networks

An efficient channel allocation policy that prioritizes handoffs is an indispensable ingredient in future cellular networks in order to support multimedia traffic while ensuring quality of service requirements (QoS). In this paper we study the application of a reinforcement-learning algorithm to develop an alternative channel allocation scheme in mobile cellular networks that supports multiple heterogeneous traffic classes. The proposed scheme prioritizes handoff call requests over new calls and provides differentiated services for different traffic classes with diverse characteristics and quality of service requirements. Furthermore, it is asymptotically optimal, computationally inexpensive, model-free, and can adapt to changing traffic conditions. Simulations are provided to compare the effectiveness of the proposed algorithm with other known resource-sharing policies such as complete sharing and reservation policies

A learning approach for prioritized handoff channel allocation in mobile multimedia networks

An efficient channel allocation policy that prioritizes handoffs is an indispensable ingredient in future cellular networks in order to support multimedia traffic while ensuring quality of service requirements (QoS). In this paper we study the application of a reinforcement-learning algorithm to develop an alternative channel allocation scheme in mobile cellular networks that supports multiple heterogeneous traffic classes. The proposed scheme prioritizes handoff call requests over new calls and provides differentiated services for different traffic classes with diverse characteristics and quality of service requirements. Furthermore, it is asymptotically optimal, computationally inexpensive, model-free, and can adapt to changing traffic conditions. Simulations are provided to compare the effectiveness of the proposed algorithm with other known resource-sharing policies such as complete sharing and reservation policies

Stochastic damage models and dependence effects in the survivability analysis of communication networks

Stochastic analyses of the Survivability of communication networks often include a simplifying assumption that failures of, or damages to, various components of the network are statistically independent. This assumption can be quite unrealistic and can lead one to conclusions that are grossly in error. Survivability analyses and syntheses of robust networks should incorporate dependencies introduced by single events that affect large geographical areas. In this paper, we construct a stochastic damage model, analyze it, and apply the results to the survivability analysis of some simple network topologies. We demonstrate how the results can differ significantly from those obtained when independence of damage is assumed. The damage model consists of a Poisson ensemble of events (damage centers) on the plane, of given intensity (level of attack), and a network resource is damaged, and hence dysfunctional, if it lies within a radius ρ (damage radius) of some damage-causing event. Statistical properties of the damage process are obtained (e.g., the covariance function, mean and variance of the damage extent on a line resulting from the Poisson ensemble) and used to evaluate dependence effects. The damage process on a line is shown to be an alternating renewal process corresponding to the busy/idle process of an appropriately definedM/G/inftyqueue, and standardM/G//inftyand Type-II counter results can thus be exploited to obtain some desired quantities