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We consider a two-tier urban Heterogeneous Network where small cells powered with renewable energy are deployed in order to provide capacity extension and to offload macro base stations. We use reinforcement learning techniques to concoct an algorithm that autonomously learns energy inflow and traffic demand patterns. This algorithm is based on a decentralized multi-agent Q-learning technique that,...
Rapid increases in mobile data demand and inherently limited RF spectrum motivate the use of dynamic spectrum sharing between different radio technologies such as WiFi and LTE, most notably in small cell (HetNet) scenarios. This paper provides a analytical framework for interference characterization of WiFi and LTE for dense deployment scenarios with spatially overlapping coverage. The first model...
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