Abstract
We witness actually huge wireless traffic demand on a limited bandwidth. This leads to develop complex and power-hungry network technologies that are often harder to manage. Thus, some core network features as radio resource management (RRM) introduce important issues as scalability and energy efficiency. This paper debates on next generation wireless cellular network radio resource distribution (RRD) algorithms. We leverage software defined network (SDN) benefits by proposing algorithms on demand (AoD), which aggregates several schedulers at the network controller. Based on Markov prediction, a real time context data analysis adapts the most suited RRD scheme at the evolved Node B. This choice depends on cell status (load, interference, etc.), thanks to the device programmability feature of SDN. Moreover, AoD reduces power consumption by optimising always the transmission rate. Simulations show that one can approach fifth generation (5G) radio policies by AoD theory with quality of experience and low carbon footprint as benefits.
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