|Speaker :||Qiong Liu|
|Time:||3:00 pm - 4:00 pm|
|Location:||Room 4A467 - Telecom Paris|
In this talk, we discuss the stability region characterization of a random network when a traffic model is integrated into the network geometry description.
First, we characterized the stable coverage probability of a random network. Starting from the notion of dynamic coverage probability, the interaction between the queue states in the network is taken into account using discrete Markov chain modelling of the queues, where the typical user’s service rate depends on the dynamic coverage probability.
A more fine-grated description of the phenomenon is made by answering the question, “what is the proportion of unstable queues in the network?” In this case, the notion of epsilon-stability is exploited, which describes the set of traffic intensities for which a queue taken at random has a probability of diverging less than epsilon.
Finally, the characterization of the stable region by considering the resource allocation is very difficult to obtain, because of the dependence between the geometry and the network’s dynamicists and the allocation strategy. However, the dynamic nature of the network considered lends itself perfectly to description by a Markovian decision process, for which reinforcement learning technologies can be proposed. We therefore study the stability-cost characteristic of the network operation according to the transmission policy.