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This paper puts forwards an on-line reinforcement learning framework for the problem of traffic offloading in a stochastic Markovian heterogeneous cellular network (HCN), where the time-varying traffic demand of mobile terminals (MTs) can be offloaded from macrocells to small-cells. Our aim is to minimize the average energy consumption of the HCN while maintaining the Quality-of-Service (QoS) experienced...
The cross- and co-tier interference creates the challenges to facilitate the concept of heterogeneous cellular networks (HCNs) in practice. In this paper, we establish a combined learning framework to autonomously mitigate the destructive interference. The macrocell is modeled as the leader and protects itself through pricing the interference from small-cells, which are the followers in the stochastic...
This paper investigates energy efficiency for the two-tier femtocell networks through combining game theory and stochastic learning. With the Stackelberg game formulation, a hierarchical reinforcement learning framework is developed to study the joint expected utility maximization of macrocells and femtocells. The macrocells behave as the leaders and the femtocells are followers during the learning...
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